Artificial Intelligence Based Diagnostics, Therapeutics and Applications in Biomedical Engineering and Bioinformatics

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Artificial Intelligence Based Diagnostics, Therapeutics and Applications in Biomedical Engineering and Bioinformatics

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Kartik Krishna Bhardwaj⁎, Siddhant Banyal⁎, Deepak Kumar Sharma† Division of Instrumentation and Control, Netaji Subhas University of Technology, (Formerly known as Netaji Subhas Institute of Technology), New Delhi, India⁎ Division of Information Technology, Netaji Subhas University of Technology, New Delhi, India†

7.1 ­Introduction The dynamics of the medical and bioinformatics sectors have been experiencing a revolution due to the integration of computational systems and artificial intelligence into various domains. This chapter aims to provide a knowledge-based backdrop for the core artificial intelligence and computer vision principles used in the biomedical sector, followed by practical industry applications.

7.1.1 ­Contemporaneous Medical Science Advancements and Trajectory The advent of modern technological advancements in artificial engineering, machine learning, and the Internet of Things (IoT) has asymmetrically changed the prospects of biomedical engineering and bioinformatics. The scope of human error has always prompted a revitalized investment in development of technology in the fields of diagnostics and intelligent designs through implementation of neural computing, expert systems, fuzzy logic, genetic algorithms, and Bayesian modeling for optimization. AI subfields are being used to solve complex problems in biomedical engineering; these include neural networks, evolutionary computation, computer vision, robotics, expert systems, speech processing, planning, machine learning algorithms, natural language, fuzzy systems, and hybrid systems. Artificial intelligence (AI) or machine intelligence is the intelligence exhibited by machines in contrast to the natural intelligence demonstrated by humans and other living beings [1]. From a colloquial perspective, the term artificial intelligence is used when a machine exhibits cognitive functions that are associated with human characteristics including, but not limited to, learning, problem Internet of Things in Biomedical Engineering. https://doi.org/10.1016/B978-0-12-817356-5.00009-7 © 2019 Elsevier Inc. All rights reserved.

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solving, ­reasoning, and perception. There are a myriad of avenues in biomedical engineering, with the core ones being: (1) feature selection, (2) visualization, (3) classification, (4) data warehousing and data mining, and (5) analysis of biological networks. The challenge of high dimensionality in computational engineering can be solved by feature selection for biomarker identification that chooses a subset of features that are significant and relevant to a specific application. Integrated feature selection methods have been developed for the same, including but not limited to Gaussian mixture modeling coupled information gain (GMM-IG), feature ­selection–­multilayer perceptron (FS-MLP), and two-step feature detection from gene selection data. In biomedical imaging and computer vision arenas, artificial intelligence develops models and algorithms based on geometrical, statistical, physical, functional, etc. models and then by using image datasets. This aims to solve a plethora of problems such as feature extraction, segmentation, imageguided surgery, visualization, computational anatomy, computational physiology, telemedicine with medical images, etc. Further, this has led to application of digital image processing in techniques for extracting, analyzing, and categorizing brain tumors from magnetic resonance imaging (MRI). Clinical data warehousing (CDW) is being used in cataloging and effectively supporting medical knowledge discovery and clinical decision making in the area of traditional medicine and complementary medicine. Advances in computer vision have given rise to CAD-based (computer-aided diagnosis) diabetic retinopathy (DR) employing deep neural networks (DNNs); this includes application of a multilevel optimization measure that incorporates preprocessing, adaptive learning–based GMM. Since the early 1960s, predictions have been made regarding the prospective emergence of powerful systems for dealing with a plethora of medical and biological datasets. Previously, many successful systems were created in various other areas of interest; however, they could not be replicated in the biomedical sphere. The early computing programs worked well with well-defined terms, such as in the case of physical models that can be mathematically modeled as in engineering and physics. Biomedical science differs from physical science as it encompasses terms that are well defined and others that are more abstract and only incompletely understood, by extension making its mathematical modeling complex. Thus this created an avenue for development of new techniques to engage in biomedical applications. Algorithms under the ambit of pattern recognition were developed for the computer to search for data patterns; this differs from the contemporary pattern recognition that employs image processing. These detected patterns can be clusters of parameters that can be used to identify and detect certain diseases in medical diagnostics. A plethora of successful systems were developed under the aegis of pattern recognition techniques in the period of 1960–80 [2]. Cohen developed a supervised learning approach using orthogonal polynomials that allowed data classification into multiple categories [3]. This was a pivotal innovation in analysis of chromatographic data that helped in detection of bacterial infection detection in patients. Deedwania developed exercise test predictors of ambulatory silent ischemia during daily life in

7.1 ­ Introduction

stable angina pectoris [4]. The study concluded that risk of silent ischemia could be accurately identified in stable angina patients using the selected exercise parameter. Raeside and Chu developed a pattern classification approach in electrocardiography that utilized discriminative features generated by Fourier analysis [5]. Preliminary work in 1972, such as the article by Gorry, triggered revitalized interest in development of AI techniques in medical systems, Gorry and Barnet represented an algorithm for diagnosis using conditional probability for every prospective diagnosis that may be estimated at each data collection set [6]. Further, the associated drawback in the colloquial pattern recognition approach was one reason for the significant advancement in the field. These drawbacks included the black-box nature of programs that provided only a final result, with minimal or no insight/explanation of the conclusion. A multitude of programs were developed to reduce the magnitude of the number of questions asked to the patients for diagnosis; this largely used binary responses from the patient, which may cause critical failure by not asking pertinent questions that may be pivotal to recognition of the problem. It was believed that using AI would resolve these impediments by inclusion of expert systems and tackling the shortcomings. For nearly a decade the AI-based systems were promising but possessed limited practical implementation and results. Erstwhile several successful systems emerged, including MYCIN. MYCIN was a computer-based medical consultation system, based on an expert system that was designed to assist doctors regarding clinical decisions over selection of relevant therapy in the case of infections. From 1980 onwards, neural network–based models emerged in contrast to the AI-based system; these models were similar to early pattern classification techniques where the knowledge was derived from data rather than experts.

7.1.2 ­Motivations for Use of Artificial Intelligence in Biomedical Sciences In addition to the impediments identified in the previous section pertaining to development of an appropriate medical paradigm to resolve blackboxing in biomedical systems, another major difficulty identified was in management of medical records. Medical data is intrinsically complex owing to multiple parameters and diverse range of parameters, including but not limited to: quantitative test results such as heart rate, respiratory rate, and temperature, blood tests, genetic tests, culture results, analog outputs such as electrocardiograms and electroencephalograms, pictorial output such as radiographs, computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine scans, and ultrasound, handwritten notes from the physician and other diagnostics-related information. These handwritten notes formed a vital part of the medical record as they included specific test results, medical history and clinical findings. In the last 50 years, several scientific initiatives have been taken to organize this diverse dataset in a format that can be easily accessed and automated. In 1982 the Comprehensive Medical Information System for Ambulatory Care (COSTAR) was developed in the Massachusetts General Hospital, Harvard Medical

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School. It is a computer-based medical record storing system designed to replace the traditional paper medical record storing system. This is a centralized and integrated model that enables transferability and allows the physician and nurse to enter medical or financial information on a single source document using a practicespecific directory of terms [7]. An early EHR system, the Problem Oriented Medical Information System (PROMIS), was developed at the University of Vermont in 1976 by Dr. Lawrence Weed. The information system enabled the users to access a medical record within a large body of medical knowledge, this focused on organization of medical data and feedback of medical action. The ARAMIS system, developed in the 1970s at Stanford University, was based on PROMIS with added features like time-oriented data to track progress of the patient and development of causal deduction-based diagnosis. Further, relatively advanced systems like the MEDUS/A and HELP programs were developed to assist in advanced decision making in the domain of medical diagnosis. Still the struggle to computerize medical data was a prevalent topic for deliberation in the medical and technical fields and was widely discussed in conferences. Introduction of modern graphical techniques allowed inclusion of image-based data into the medical record of the patients. With the arrival of the Internet, the focus now shifted to standardization of medical records so that they could be accessed anywhere and delocalized. With the advent of new technologies came new problems associated with security, information sharing, privacy, and standards. Due to lack of uniform growth geographically, paper-based medical records were used in many localities, which resulted in lack of system-wide coherence. As identified in the previous section, a strict paradigmatic approach to facilitate decision making was not successful due to the lack of appropriate mathematical modeling for biological systems. The nondeterministic nature of the models led to the development of pattern recognition techniques to enable classification and to address problems in differential diagnosis. These approaches were data based, hence they allowed the computer to search for patterns in data. Purely data-based approaches have some issues associated with them, primarily that the model is heavily dependent on the accuracy of the data and is limited by the general applicability of the data. First, this not true with medical records, as there is some extent of error in medical diagnoses that may be added during mathematical calculations of the data during the modeling. Secondly, the problem regarding applicability is a practical problem. For example, a model developed for female members between ages 25 and 35 of a particular locality in a city cannot be generalized for the entire population. This can be verified from a study done on heart diseases in Finland, which could not be applied in the United States owing to a more diversified population. Knowledge-based propositions averted these problems by using expert input at the knowledge base. However, this approach too had some inherent flaws. When several expert consultants are used to produce the knowledge base, differences of opinion can result in knowledge bases that are not in agreement with each other. Further, knowledge-based systems need to have new methods constantly developed to account for the new knowledge due to advances in the field.

7.1 ­ Introduction

7.1.3 ­Introduction to Artificial Intelligence and Computer Vision in Biomedical Sciences The high level of growth in biomedical sciences and bioinformatics and rising challenges (as covered in the previous section) pushed for further developments in the artificial intelligence domain. The focus of study has been diverted from individual molecules to studying the interaction of complex molecules and cells that control the entire structure. This created an environment for collaboration between engineering, bioinformatics, and biomedical engineering. This led to development of systems biology and systems medicine that focuses on understanding system interaction with time and space to enable understanding of functioning (Fig. 7.1). The development of artificial intelligence under the ambit of computer science gained momentum in the 1950s, as it was used to deal with conventional problems that were deemed difficult by computer scientists, through use of data and ­probability-based tools. AI has been a promising avenue in a diverse array of research domains for solving complex problems. There has been a revitalized curiosity about the application of AI techniques in the domains of bioinformatics and biomedical engineering, ranging from knowledge-based disease classification to a unique way to treat diseases and medical diagnoses (Fig. 7.2). Fifteen years after the inception of AI, its development in biomedical engineering began, as early AIM (Artificial Intelligence in Medicine) researchers worked in the area of life sciences, as evident in the Dendral experiments [8]. The Dendral project was a large-scale program to use

1 The identification of system elements and component

General system of system biology and medicine

3 The understanding of systems: The extraction of emergent properties of biological systems by means of analysis of the structural properties and dynamics of systems:

FIG. 7.1 General system of system biology and medicine.

2 The modeling of systems: The development of appropriate models that represent the physical and functional structure of biological systems and the complex interactions within the system;

4 The control of systems: The identification of control targets and the development of appropri-ate approaches that regulate the behaviour of the targets

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Core topics of biomedical engineering and informatics

Feature selection Visualization Classification Data warehousing and data mining Analysis of biological networks

FIG. 7.2 Core topics of biomedical engineering and bioinformatics.

task-specific knowledge of a problem domain through application of heuristic programming to experimental analysis in empirical science. The interest increased exponentially with the introduction of the SUMEX AIM computer resource. Stanford University Medical Experimental Compute (SUMEX) for Artificial Intelligence in Medicine (AIM) network was developed as a nationwide shared computing tool dedicated to developing AI uses in biomedical engineering. This made use of dormant ARPANET technology to make the computer cycle accessible. Computer vision is a field of image analysis that aims to enable computers to understand images. It is one of the core fields of machine learning, which is effectively defined in computer science as a system’s ability to learn. This evolved from the field of pattern recognition in computational theory. ML creates programs that can learn from a large dataset and make predictions. The fields of computer vision encompass object detection and tracking, automatic document analysis, 3D reconstruction, computational photography, augmented reality, image recognition, face detection and recognition, and medical image processing. One focus of application is digital image processing techniques to extract, categorize, and analyze brain tumors from MRI [9].

7.2 ­Principal Disciplines of Artificial Intelligence and Computer Vision 7.2.1 ­Neural Networks Machines are creations of humans made to carry out the activities that otherwise might be out of reach of human abilities on a mental or a physical scale, or simply made to make a process easier, more efficient, or faster than manual methods.

7.2 ­ Principal disciplines of artificial intelligence and computer vision

Traditionally, machines have operated on a principal distribution of the mechanical apparatus into various parts, each having a particular functionality in a predefined order, which altogether perform a particular task that is well studied and specifically targeted. In traditional machines the associated functionality is frozen at inception in terms of response to the environment. This design has worked well for machinery of varied sizes, functionalities, and applicabilities. But as advancements in various fields have taken place, there is a need for updates in the way machinery is perceived in its structure and functionality, with the demand to incorporate a dynamic response to environmental variables and selfimproving mechanisms being deployed in today’s modern mechanical systems [10]. This gives rise to the need for solving problems on the basis of perception of the very basic nature of the problem, rather than the solution to a statement that is presented to us. This involves generalizing the work at hand and performing tasks toward their solution in a manner of predictive intuitions, leading to the required solution, drawing on the experience that the machinery has gained from the previous tasks it has performed. Such an approach sounds very familiar in many ways and is eerily similar to the action chain a human being employs to see through a task at hand. This similarity is drawn fairly literally when working with the previously described scheme of mechanical implementations. The said perception is based on the instinctual approach of the brain toward a solution of an assigned task, the structure of the computational background required for such perception borrowing that of the neuron-based interconnected network and its functionality closely resembling the information transfer and stimuli response to the same, giving rise to what is known as neural networks or artificial neural networks, to be more precise. A neural network is a machine that is built to function in a way similar to that of operations of the brain in the accomplishment of a particular task or functioning at hand. Drawing parallels to the structural and functional attributes of the brain, a neural network performs computations to find a solution using neuron-like processing units, with the net of interconnections between such computational components realized just as the interconnected mesh formed by neurons to provide distributed computing for processing of the huge amounts of data involved. Such a network is either realized using electronic components, with the structure closely mimicking the structure of the brain, or is digitally implemented using software simulations, serving the same purpose [11]. Functionally, a neural network is a collection of many interconnected simple processing units, with the ability of storing knowledge gained from a learning process through experiences and using the knowledge for future applicability in problem-solving. When all is said and done, it should be understood that a neural network is not itself an algorithm but rather a framework that a learning logic employed in machine learning will follow for its implementation. The approach used to carry out a learning process is called a learning algorithm, which is a mathematical representation and

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instruction set for the functioning of the neural network, giving commands for the way the variables involved change and the sway over the output changes in the governing set of equations. The described process is known as the variable definition and synaptic weight updating, which a learning algorithm looks to implement. Change of topology of the neural network, if needed, is also governed by the learning algorithm. Functioning of the nervous system can be broken down for simplification, to understand the building of the structure of artificial neural networks. The stimulus (signal) is transferred and picked up by the system receptors, and then is processed by the neural network, which crunches up the provided information and formulates the needed actuation toward the given situation. This required action is passed on for implementation to the part of the body that needs to act in the form of an information impulse, which then, through effectors, is relayed through the part of the body to see to the actualization of the required solution, leading to the response of the given stimuli [12]. Drawing on this model of nervous system functioning, a neuron is designed. Identifying the basic elements of a neural network model, we have: • Synapses: Connecting links between the input to the neuron inferential unit, each link being characterized by the strength or the weight that it holds toward the actuation. This weight surmises the hold of sway that each input has toward the output of the inference to a particular stimulus, that is, the given problem at hand. • Adder: It is a linear combiner, a summing point for the weighted input signals that give a characterized and summed-up input to the function governing the relation between input(s) and output(s). • Activation function: The principal part of a neural design, an activation function, also called a squashing function, is a nonlinear complex functional mappings that relate inputs and response variables involved in a problem. Their structure might depend on their functionality that the neural network is poised to serve and the algorithm that it follows [13]. Working with a neural network involves two steps: the learning phase and the testing phase. In the learning phase, a test data with given desired output is provided for the network to assimilate the response structure that different kinds of problems hold. The test phase is where the network is employed to gain meaningful inferences based on a situation provided, with the decision and actuation process being dependent on the knowledge gained during the learning process. We will learn more about learning in the following sections.

7.2.2 ­Machine Learning Machine learning can be defined as the field of study of algorithms that make machines capable of decision making and actuation without being explicitly designed to do so. Functioning of machine learning algorithms can be best understood by definition of a “well-posed” mathematical and relational learning problem, as given by Tom

7.2 ­ Principal disciplines of artificial intelligence and computer vision

Mitchell in 1997, stated as “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E” [14]. So, learning basically is employed on a particular task to improve its performance on similar tasks, gaining experience by repeating such tasks, and with each employment, fine-tuning its parameters to improve its performance on test tasks, hence improving the accuracy of output prediction. The objective of ML is development of programs to guide machines to access data and use it for learning said task on their own. The learning process starts with observation or data, in the form of direct experience or as instructions, building up a knowledge base through this process, using this for finding patterns and making decisions for problems posed to it [13]. The most important part of the process here is learning. Learning can be categorized broadly into three types based on the training dataset used and how it is inferred for the learning process: supervised learning, unsupervised learning, and reinforcement learning [15].

7.2.2.1 ­Supervised Learning

This learning technique basically works on the simple principle of application of knowledge gained in the past, possibly from different sources, for prediction of future events with new datasets, using labeled example data from past experiences. Here, in the training process, a known dataset is analyzed, using which the learning algorithm then produces an intuitive function to make predictions about output values for the posed problems. After sufficient learning, the algorithm is also able to set target parameters for new inputs of posed problems. Improvement in the predictive analysis along with the said target setting for output is done by fine tuning the algorithm’s activation function by comparing its output with the correct ones for learning data. This mode of learning bears resemblance to human learning under supervision of a teacher.

7.2.2.2 ­Unsupervised Learning

In this learning technique, the algorithm learns from uncategorized and unlabeled examples for training data and associated target responses that contain numerical values or string labels. Predicting later correct responses when posed with new problems, the learning scheme tries to restructure the new data in the form of earlier processed data, formulating the same patterns as mined in the training dataset to reach the output of the problem at hand. Unsupervised learning is used to provide some form to random data and, if possible, find some meaning in such random data. They are even used to find new dimensions for the supervised learning techniques, like finding new features that might represent a class or new unrelated values. This scheme of learning borrows from the activities of the human brain while employing methods to extract meaning out of observed objects or events that are similar in structure to earlier observed things, say by comparison of degree of similarity of objects.

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7.2.2.3 ­Reinforcement Learning

In this learning technique, just like unsupervised learning, the algorithm is presented with a dataset that lacks labels, though accompanied by positive or negative feedback to the outcome that the algorithm presents. It resembles the trial and error approach toward a problem. Error helps the algorithm learn and adjust its parameters accordingly, teaching the machine the less likely approach toward a problem.

7.2.3 ­Classification and Regression Predictive modeling is the class of ML problem in which a model needs to be developed for a prediction on new data provided by the input. Predictive modeling describes a mathematical problem of approximation by mapping a function (f) from the input variable (X) stated by the problem to a set of output variables (Y). It is known as function approximation. A classification predictive model is tasked to approximate a mapping function (f) from input variables (X) to discrete output variables (Y). The output variables are known as labels and categories, while the mapping function is used to predict the class or category which a given observation belongs to. A regression predictive model is tasked to approximate a mapping function (f) from input variables (X) to continuous output variables (Y). The continuous output variable is real-valued, such as an integer or floating-point value, the quantities that they represent often being amounts or sizes.

7.2.4 ­Predictive Analysis Predictive analysis pertains to the group of statistical techniques from data mining, machine learning, and predictive modeling, that analyze the currently known facts and gained knowledge to predict the outcome for future events or otherwise unknown occurrences [16]. It is widely used in the fields of commerce, financial services, marketing, telecommunications, retail, travel, mobility, child protection, healthcare, capacity planning, social networking, etc. The process of predictive modeling involves various steps involving different analytical procedures. While being posed with a problem, we need to understand the structure of that problem, defining its objectives and identifying the deliverables that it asks and identifying the datasets that we need for analysis. Next comes the process of collecting relevant data. Data is mined from multiple sources and compiled for the predictive analytics to be applied to it. This shall give us a better coverage of the objective of study [17]. Now, the collected data is analyzed by inspecting it, cleaning, and modeling it in a usable format, with the goal to extract out, from this processed data, valuable information toward the problem assigned and its indicative solutions. Next we scrutinize this extracted information out of processed data with help of statistical tools, so as to build a hypothesis toward the projected outcomes or validate

7.2 ­ Principal disciplines of artificial intelligence and computer vision

them, if some already exist and test their accuracy with some more standard statistical models. Once we are able to validate a hypothesis governing the given system, modeling is done. This is the most crucial part of the predictive analysis procedure. It provides the gateway for self-regulated process development, giving the closest to accurate outcomes to newer problems that might be encountered in the future. Provisions can be made to choose from the best answer out of many solutions in the case of a multimodal evaluation model. After development of the model, its deployment is important. It is necessary that the nature of the tasks that the model is hardwired to do matches the credentials of the analytic network model that it has been based and built on. Deployment shall ensure that required deployed analytics serve the purpose of decision making and automated output generation in the set-up for which it has been put in use. At last, the model performance is monitored to insure the output that is expected out of the analytics is being generated to an acceptable level of accuracy.

7.2.5 ­Evolutionary Computations Biological studies are known to churn out specific sets of patterns of different kinds that might be used in aiding our quests in the domain of neural networks. The study of behavior of different organisms, in their routines of various kinds, have churned out similarities in the approach that these organisms take while carrying out their existentially necessary functions of different kinds and our artificial learning procedures. Hence, they pave the way for incorporation of such patterns in the ML algorithm development. This array of algorithms is known as evolutionary computations or genetic and swarm algorithms [18]. Technically speaking, this array of algorithms is a metaheuristic approach toward problem solving, in a trial and error fashion of solution searching, driven by a population based functional analytics. Given the scope of the field we are dealing with here, as it is well known and documented that nature is full of patterns, evolutionary computations naturally can be subdivided into many categories. To name a few, there are particle swarm optimization algorithms, genetic algorithms, artificial immune system algorithms, biological evolution algorithms, etc [19]. Particle swarm optimization algorithms try to find an optimized solution by an iterative approach toward finding and improving solution. It is based on the space search methods for different objectives acted out by different organisms, in most cases for finding food or a mate and to safeguard against enemies. The working of these algorithms basically depends on, and varies, with each other on the basis of the exploratory behavior, which allows it to search for optimum solutions in a broader search-space, increasing scope of accuracy, and exploitative behavior, which means a more intensive local search for local optimum, gouging against false solutions.

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Ant colony optimization algorithm is the most well known particle swarm algorithm. This is basically an optimized path-finding technique mostly employable through graphs, used to solve computational problems by a weighted probabilistic approach, imitating the food searching routine of ants, traveling to the destination once found through the optimized path, reaching this path by a calculated probabilistic trial method [20]. The theory of natural selection as proposed by Charles Darwin inspires the basis of genetic algorithms, that employ metaheuristic search techniques for finding the optimum solution to the problem at hand employing the evolutionary selection process. Its algorithm deploys two representative models, a genetic representation of the solution domain, that models the problem in the required form and hence giving an outlook into the prospects of the problem, and a fitness function, which evaluates the merit that the derived solution holds toward the requirements of the problem at hand, working toward increased accuracy [21]. The immune system has the property of memory, retaining the account of the past encounters it has had with previous pathogens and learning mechanisms against various infections. Genetic algorithms employ a similar approach to carry out predictive analysis, using the stored knowledge gained over experience of different problems and dealing with them, employing the methods used earlier if they present themselves again [22].

7.2.6 ­Computer Vision The ability of a machine to perceive visual data and extract information out of it by analysis in a manner similar to that of a human brain is known as computer vision.

7.2.6.1 ­Basic Functionality

Computer vision, as defined previously, deals with analysis of an image, with the aim of finding different components of an image, with input being an image and output being the interpretation of an image, much like an imitation of the human eye–brain system. An image can be thought of as a variation of brightness and color, spread over a space. This helps in differentiation of characteristics of an image, which is done by classifying each pixel through various methods into groups, spotting edges and hence extracting features of the image through them [23]. This is much like pattern recognition, where the algorithm is trained to perceive different patterns to classify new input into predefined sets and provide an inference on the given data based on the classification that is assigned to the given input. In a similar way, the subject in question is processed pixel by pixel, each pixel being segregated according to the training of the algorithm used to segregate different kinds of pixel contrasts, judging the edges of the image by clustering similar pixels, extracting the information based on this segregation, and hence inferring output data based on the visual input provided, thus completing the job at hand [24].

7.3 ­ Artificial intelligence and computer vision

7.2.6.2 ­Typical Tasks

Industry has been waiting for this tool, with such technology being in demand for decades. Its fulfillment is only occurring now that ML and related technology has seen advancements on the huge scale that is currently being experienced. Naturally, computer or machine vision has instantly found varying applications on an industrial scale since its introduction, the level of implementations ranging from inspections in production lines for flaws in goods to sophisticated robot-based applications involving complex tasks, often heavily dependent on the computer vision technology. Some of the varied tasks that are being handled are: automatic manufacturing inspection, human assistance in complex object identification as in species identification, space objects identification, process control as in the case of industrial robots, visual surveillance, event detection, counting, human-machine interaction interface, object modeling, autonomous vehicle navigation, and information organization as in image sequencing and visual data indexing [24].

7.2.6.3 ­Medical Applications

One of the most prominent and important uses of computer vision is medical image processing. Medical images are often complex to process, with even experienced medical practitioners being prone to errors in inferences, given the complex nature of such images. The primary aim in this application lies in extraction of information from image data for diagnosis of a patient. Applications where computer vision is employed in this field are of grave importance, such as tumor detection, arteriosclerosis and other bodily changes, organ dimension measurement, blood flow analysis, etc. Computer vision aids medical research by provision of insights into human anatomy, otherwise complex to understand, such as brain structure. Medical treatment effects can also be studied using such methods. Medical applications of computer vision also include image enhancement for traditional image-based diagnostic procedures that are heavily dependent on image interpretation, such as ultrasonic images or X-ray images, reducing noise influence.

7.3 ­Artificial Intelligence and Computer Vision: Biomedical Applications and Solutions 7.3.1 ­Medical Imaging Medical imaging encompasses electronically aided visualization of interior organs for analysis and study of anatomy, and techniques to analyze and bring meaning to these visual representations, aiding clinical remedy and intervention [25]. The internal structure of the body and physiology are important for many diagnostic purposes, though they are difficult to image, which creates obstacles in the process. Medical imaging gives a much-needed in-depth visualization, improving the perceptibility of medical conditions. The most useful area of application for imaging in the field of medicine is that of oncology, the study of tumors and cancerous tissues. This field often demands

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removal of infected tissues from healthy ones, requiring a clear demarcation between them. Even detection in the form of abnormalities requires imaging, giving accuracy in the field, which is a highly valuable feature in the field of medicine. The technology uses images captured by different medical procedures such as electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), etc. These images then undergo various processing methods in order to provide critical and detailed analysis as required by medical intuition about the condition being tended to [26]. Medical imaging is often attributed to be a noninvasive way of producing internal anatomical images using a set of guiding techniques, a perception that has been the main selling point of this technology in various fields of medicine. Noninvasive here applies to any procedure using instruments that do not pierce the body of the patient, which is the case for most imaging techniques in use. The following sections discuss some popular applications of medical imaging.

7.3.1.1 ­Head/Neck imaging

A tumor is an undesired growth of cancer cells, which might grow in any part of the human body. There are many kinds of tumors, each having a dedicated and diverse medical treatment. Hence, identification and segregation of different kinds of cancerous tissues, with specificity in the ailment, is of paramount importance. Brain tumors are classified into two basic types, known as primary brain tumors, the ones that originate in the brain and tend to remain there, and metastatic brain tumors, which originate as cancerous cells in other parts of body and later spread to the brain. Brain tumors can be further categorized into two types based on their criticality, namely benign and malignant. The criticality of a tumor and its classification into these types depend upon many factors, like age, sex, race groups, etc. Medical imaging is used in all the medical necessities that brain tumor study and treatment include. Now, before the analysis of brain tumors and their study, we need a medical image of the body parts that need to be analyzed. This brings us to the image modalities, the importance that they hold in the process, and the accuracy that is aspired to be achieved. There are many current imaging modalities, the popular ones being X-ray, ultrasonography, computed tomography (CT), magnetoencephalography (MEG), electroencephalography (EEG), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) [27]. Among all these modalities, MRI is the one that has gained the most traction in the field over the years. MRI technology is a nonintrusive technique of imaging that creates good contrast for soft tissues, giving an invaluable amount of information about the shape, size, and locality of the brain tumors, with no risk to the patient from exposure to high ionization radiation, which most of the other technologies use for imaging. Once we have the generated image for the required tumor-affected area, the ­image must be processed and refined. This preprocessing is the pivotal step of the

7.3 ­ Artificial intelligence and computer vision

functionality of medical imaging and the one that directly affects the demarcation of malicious tissues from healthy ones, when talking in terms of brain tumor segmentation. The raw MRI image now needs to be preprocessed so as to refine the detailing and suppress the distortions, if any. Tasks like image noise removal, skull striping, error delineation, intensity normalization, and skull area removal are part of the preprocessing steps carried out. Next come the actual methods of segmentation realization, or the brain tumor segmentation methods. Brain tumor segmentation can be categorized into distinct complexity brackets, depending on the different techniques employed for the functionality. Manual brain tumor segmentation involves experts analyzing the brain tumor images without any help from the inanimate, purely on the basis of the knowledge they possess. Here, the expert needs a very high knowledge of anatomy as well as segmentation knowledge itself, increasing the responsibility on the expert and making the procedure time-consuming and unreliable for long-term assessment, yielding poor results in terms of accuracy of the judgment. Semiautomatic tumor segmentation emphasizes the interaction between user and computing software, with the user providing the necessary input parameters and software analyzing the visual data it is provided with based on the parameters as stated by the user, providing a realization into the image and a detailed synopsis of the image as segmented. Using this synopsis, the user is then able to make further inferences. The interaction between the user and the software that computes the algorithm to be run is the main focus of this type of brain image segmentation. The segmentation process can be largely demarcated in three major steps, namely initialization, feedback response, and evaluation. The problems that arise here revolve around the different inferences and conclusions that different users might end up with for the same problem, due to manually selected parameters and output analysis differentiability. Fully automatic brain tumor segmentation uses a computer for segmentation purposes, with its own knowledge base to set the parameters needed, acquired through usage of machine learning and artificial intelligence techniques. The simulation of human intelligence surpasses the need for a user to analyze the segmentation result and for the segmentation criteria setting. This leads to higher accuracy, eliminating the possibility of human error altogether. The current automatic segmentation methods can further be divided into three major categories: conventional methods, classification and clustering methods, and deformable model methods. Conventional methods use the standard image-processing methods and are commonly employed in two-dimensional images. Threshold-based and region-based methods are the two segmentation methods falling into this category. The threshold-based method employs comparison of the intensities, taking one or more intensity thresholds as a base. Threshold technology is best suited to be applied on images with high contrast in homogeneous objects, where background is high contrast. It works out to be the best segmentation technique for such images,

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segmenting the objects and the background. Low-contrast images might become a bit too difficult for processing using this technique. The region-based segmentation method operates on a pixel-by-pixel level of an image during analysis, forming disjoint regions by merging homogeneous pixels in the immediate neighborhood of each other, following a stated definition for similarity. Region growing and watershed segmentation are two main functionalities of region-based segmentation. Region growing employs fuzzy-based selection of a region of interest to be examined, and similarity with adjacent feature grains is examined so as to give a structure disjoint from others, which is quoted as a segment of the given image. The watershed segmentation works on the trickle-down principle used in dams and reservoirs (hence the name). A hierarchical structure of pixels bound by the idea of similarity, based on the criterion as provided, decides on the segmentation of the image [28]. The classification and clustering methods use ML for decision making processes, automating the dependencies the earlier segmentation methods had on human intelligence for providing the parameters to work on. The process here has the ability to learn, which allows the algorithms to make out definitive patterns from raw data and remember the steps that might be involved in complicated subprocesses involved. This method can be classified as a supervised learning method, semisupervised learning method, and unsupervised learning method, all of which have been discussed before for further understanding. The classification algorithm is a supervised learning method, in which the input observations are the stimulus and the output observations reactions, going by the hypothetical neural model. The clustering algorithm is an unsupervised method, where the input is not a labeled one and problem solving is based on the experience that the algorithm gains out of solving similar problems as a training schedule. The deformable model method works on the 3D MRI data, where the challenge lies in extraction of boundaries in the given scenario and giving a structure to the model being worked on, improving upon the lack of ability to perceive a complex model in 3D space. The deformable models bring in intuition toward the model generation and analysis for the given image segment production [29]. The various methods discussed each have their own merits and demerits, finding applicabilities in varying scenarios, making it the user’s responsibility to understand the needs that the segmentation problem holds and to choose the best algorithm for the given problem, analyzing the pros and cons of each method.

7.3.1.2 ­Mammographic Imaging

Breast cancer is a rampant and deadly health problem often encountered in contemporary times. The success of breast cancer detection is very dependent on the quality of mammographic images taken. The optimum level of image quality and hence the best imaging equipment is therefore needed. With a given suitable image, there are many methods that can be employed to analyze a mammogram for abnormalities, often using a series of heuristics, such as filtering and thresholding. The heuristic methods employed are known to have a low robustness for a large number of the images to be classified [30].

7.3 ­ Artificial intelligence and computer vision

Introduction of statistical methods to these models helps to deal with the robustness problem while coping with the larger number of images to be processed. As we have discussed in the previous section, there are many medical image segmentation techniques, though for mammographic purposes, these need to be tweaked to incorporate clinical constraints and parameters. It is important that robustness and effectiveness of algorithms is tested through an evaluation procedure with given standards. A clinically validated approach to the problem is necessary to be ensured of an objective evaluation of the segmentation method used [31]. Research has shown that ideal data and real clinical data use a different set of parameters in processing and analysis, hence giving indications that parameters need to be evaluated for the given segmentation method used for image analysis. It is hence difficult to pitch different methods against each other for comparative study. Other methods in the field to increase accuracy in detection include usage of metrics based on the distance between boundaries of segments for evaluation and analysis tests. Using parameters derived from the boundaries for comparison and accuracy checks has also been proposed. Pixel-by-pixel comparison methods have also been developed, with pixels encompassed by two different boundaries being under test.

7.3.2 ­Pattern Recognition 7.3.2.1 ­ECG and Other Cardiovascular Analysis

Heart problems can be detected at an earlier stage by monitoring of heart pulse rate. Heart rhythm changes is strongly indicative of irregular heart activity and hence heart disease. The group of conditions dealing with irregularities in heartbeat pattern is known as cardiac arrhythmia. Disruption in electrical impulses that guide the heartbeat rate and rhythm is the root cause of such activities, and hence the factor of importance and the surveillance cornerstone in cardiovascular analysis [32]. Electrocardiography (ECG or EKG) is a convenient process to study the irregularities in the heartbeat patterns. It is a noninvasive process that records the electrical activity of the heart over some duration of time. It is the study of ECG and the correct inference from its analysis that is in focus for the purpose of these discussions [33]. The nonlinear dynamic behavior shown by the ECG signals is the key characteristic exploited in this analysis. It is known that the nonlinear component showing the dynamic changes occurring in the electrogram are more pronounced than the linear ones, preserved in the phase information, extracted by higher-order statistics and dimensional slicing from higher order spectral domains. Many schemes have been proposed for this analysis: fuzzy-based ML techniques like MIT-BIH, which is employed in classification of cardiac arrhythmia using a fuzzy decision tree; MITBIH arrhythmia, which improves on the previous technique by introduction of a new method of defining fuzzy sets, developing an adaptive ECG classifier; and further such methods that work along the same line to increase the efficiency of the process with introduction of improvements to the previously proposed methods [33].

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ML techniques based on the hidden Markov model (HMM) and rough set theory have been proposed, which have an improved classification record over fuzzy-based schemes, though they do not satisfy the medical applicability expectations for classification accuracy [34]. Neural network–based ML schemes have been proposed that basically are ANNbased (artificial neural network) classification systems for cardiovascular arrhythmia in conjunction with multichannel ECG outputs, with variations in the classification methods and neural network structure. They provide a higher level of accuracy, though they are lacking in the classification types that they can serve between the types of heartbeat patterns [33]. Employment of support vector machine (SVM) ML techniques solves this problem, bringing in a wider array of detection for different arrhythmia patterns, though having varying accuracy for each of them. This has been worked on since the earlier proposals of such schemes, increasing the individual accuracy of detection for different classes of heart rate abnormalities [35]. Hybrid algorithms have been introduced too, with NN-based schemes being optimized by other well-known ML techniques like genetic algorithms, fuzzy logic implementations, etc. Such schemes, though accurate in their domain and highly improved on a classification scale, are often specific for application to some types of pattern-recognition schemes due to the training sets they are designed for, hence serving only a limited array of abnormality detections [36].

7.3.2.2 ­Chromosome Examination

Applications of computer algorithms in genomics and genetics have been used to solve a plethora of issues in these areas. ML-based paradigms can be used to identify the location of transcription start sites (TSSs) at a genome sequence, splice sites, enhancers, promoters, nucleosomes, etc. The models can identify genomic elements along with their relative positions and build systems capable of DNA annotation along with eukaryotic chromosomes. Potential disease-causing biomarkers can be identified through the process of differentiating disease phenotypes. Furthermore, in the study of human disease susceptibility, AI-based computational systems have provided detection, characterization, and interpretation of epistasis. Effectively we can categorize the application of artificial intelligence into: Genome sequencing: For identifying the possibility of developing a specific disease and developing of corresponding therapies. Direct Consumer Genomics: This involves clinical workflow and genetic data analytics.

7.3.2.3 ­Thalassemia-Based Evaluation

Thalassemia is a blood-based genetic anomaly that involves absence of error genes responsible for production of hemoglobin; this may result in other chronic and acute problems such as tiredness, paleness in skin, enlarged spleen, and bone-related issue. The genetic disorder is inherited from paternal genes, and alpha thalassemia, beta thalassemia, delta thalassemia are major classes of the disease. The disorder ­depends

7.3 ­ Artificial intelligence and computer vision

on the type of anomaly in the heterotetramer structure of the 2α and 2β globin chain. Currently, thalassemia can be diagnosed at 10–11 weeks of pregnancy using amniocentesis and chorionic villi sampling for fetus and routine blood checkup, and hemoglobin electrophoresis and DNA testing for individuals at later stage. Early diagnosis and treatment have proven to be crucial for improving the condition of the patients over the long term. Hemocromocytometric data is collected in the first-level screening and total HbA2, globin chain synthesis, and genetic analysis are done in the second level to identify α and β thalassemia carriers in the population. The latter stage is essential due to high accuracy in results, but it is time consuming in addition to being expensive, so can prove to be difficult for the larger dataset. Intelligent techniques to effectively assess and screen the population are based on the preceding two levels. In 2003 a comparative study of k-nearest neighbor (kNN) and SVM classifiers was undertaken for thalassemia screening [37]. The dataset used in the study was from 304 students between the age of 14–15 with exclusion of anemic individuals. As per the statistics, 8.88% of individuals were suffering from β thalassemia and the remaining were a mix of α thalassemia and healthy candidates. Features used to categorize and differentiate in the study were: RBC, hemoglobin level, hematocrit, and the MCV level. Usage of the leave-one-uut algorithm maximized the information extraction from data; here each repeated data point is deleted with the remainder put into training and class predicting the deleted pattern. The methodology proposed uses SVM to classify between healthy and unhealthy patients at the first level and k-NN is used in conjecture at the next level to identify the type of disorder, i.e., α or β thalassemia. The results were extremely accurate and added to an average of 91.49% for both the stages, hence attesting to the efficacy of using AI-based models for diagnosis. Further, the study compared the performance of the hybrid system (SVM and k-NN) to performance of MLP, which had yielded lower accuracy. In 2009, Payandeh et al. proposed a unique method to diagnose and predict blood disorder and cancer using the MLP and back propagation algorithm for five types of disorders ranging from megaloblastic anemia to thalassemia [38]. They collected a plethora of data, from white blood cell counts to MHC values. The values of these were used in the neural network, which used a tan-sigmoid function in each layer; the model’s lowest accuracy was 98.1% for leukemia among the five identified diseases.

7.3.3 ­Computer-Assisted Surgery Computer-assisted surgery (CAS) is an amalgamation of 3D coordinate measurement, voxel processing, and pseudoimage presentation that helps skull surgeons navigate. This involves uses of a hand-guided electromechanical coordinate digitizer to identify points of interest. By using technical hardware and intuitive software, this enhances a surgeon’s ability to visualize a patient’s anatomy and augment the surgical precision. CAS enables the surgeon to walk through a 3D model of the prospective area for the operation, providing a holistic picture of the neighborhood and region. The first

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step involves graphical modeling of the operating region for the patient. This involves usage of medical imaging tools such as CT, MRI, X-ray, etc. through which the anatomical operating region is scanned and uploaded on the computer system. Using a myriad of dataset and data fusion tools, a 3D dataset is made that reproduces the geometrically appropriate structure of the tissue anatomical region. The next phase involves image processing analysis (similar to that discussed in Section 7.3.2); this involves extraction of relevant information from the 3D image of the region, such as information regarding tissues, bone, and arteries, by manipulating the contrast levels. Further, this is quintessential to preoperative planning, providing the surgeon a view from multiple perspectives and thus increasing the accuracy of diagnosis (Fig. 7.3). Under the preoperative phase, virtual surgery has gained prevalence in the sector. This involves a coordinated approach among surgeons, engineers, prosthetist team, and various other experts, depending on type of surgery. Using the virtual model, developed resection and margins are determined and cutting paths are identified. This enables the surgeons assess to multiple paths in the preoperative phase, allowing for determination of an optimal path that has higher probability of success.

7.3.4 ­Diagnostics 7.3.4.1 ­Parkinson’s Disease

Parkinson’s disease (PD) is a degenerative nervous system disorder that impacts the movement of the patient. The symptoms, which take time to be noticeable, include tremors, slowness, dysphonia, and rigidity in conjunction with thinking or behavioral

C-T scan of patient’s head

3D view of operating area

Position Measurement

CT slices

FIG. 7.3 Principle of computer assisted surgery.

Voxelmodel

7.3 ­ Artificial intelligence and computer vision

issues in some cases. The causes are asserted to be genetic factors and environmental triggers [39]. Conventional diagnosis techniques include a thorough medical examination from the physician that includes neurological and medical history assessment. Having several follow-up assessments increases the accuracy of the diagnosis; however, the presence of Lewy bodies in midbrain during autopsy confirms the diagnosis. Research has indicated an accuracy of 79.6% in clinical diagnosis of PD when verified with autopsy [40]. Several initiatives have been taken to increase the reliability of diagnosis in follow-up assessments; further, efforts to standardize diagnostic criteria have been pivotal in identification in early stages. The most prevalent criteria is the UK Queen Square Brain Bank criteria, developed by the National Institute of Neurological Disorders and Stroke, which employs rigidity, postural abnormality, and bradykinesia [41]. Over the years, several studies focusing on biological, chemical, and genetic areas have been published that involve the fields of computer science and artificial intelligence. Artificial intelligence techniques have played a key role in development of speech-based computer-aided diagnostic techniques for PD. ML techniques have been pivotal in medical decision support systems; these include linear discriminant analysis, regression trees, k-NN algorithm and Bayes classifier, AdaBoost algorithm, decision trees (DTs), naive Bayes, and MLP. These techniques have been used in the biomedical sector in analysis of EEG and ECG signals, MRI scans, retinal pathologies, mammograms, etc. In the case of PD, several techniques such as SVM, ANN, LDA, and fuzzy k-NN are used to support the diagnosis using dysphonia symptoms. Reduced vocal volume, monopitch, disturbance of voice quality, and irregular rapid rate of speech are symptoms used in detection of PD by ML techniques [41]. Further, several techniques based on artificial intelligence exist for detection of PD using single and multiple motor symptoms. Based on the type of symptom(s) and sensor used, a wide array of proposed diagnosis tools is available, including entropy, spectral, and fractal features. In earlier studies sensors like the triaxial gyroscope were used to record data while patients undertook predetermined activities; spectral analysis of the data collected was done to filter the region of interest (frequency of resting tremor that is 3.5–7.5 Hz) [42]. Using inertial sensors and accelerometers, electromyogram tremors were analyzed and methodologies were developed to aid in diagnosis of PD. Motor symptoms such as bradykinesia, dysarthria, dysphagia, dyskinesia and akinesia have also been used in identification of PD using ML-based paradigms to support diagnosis [43]. Image-processing techniques in diagnosis of PD have also gained significance over the years along with other conventional and unconventional approaches. As mentioned previously, there is currently no known cure of PD. However, early detection can prove to be pivotal in saving life, conventionally requiring years of experience and expertise. Using neuroimaging, early diagnosis and assessment can be improved. Statistical parametric mapping can be used to locate effect of parametric models in images during diagnosis. Various ML-based techniques are employed to analyze structural changes via the dataset gathered from PET in addition to SPECT.

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7.3.4.2 ­HIV

Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) are an array of medical issues that result from contraction of human immunodeficiency virus (HIV). This syndrome leads to deterioration of the immune system, impairing the body’s ability to keep infections at bay, of which the opportunistic infections and cancers take advantage, causing an array of diseases. Early detection of the virus in a host’s system leaves scope for controlling the damage done. To boost this functionality, ML is one of the latest additions to HIV diagnostics. The study of propagation of infection spread and behavioral response of various other viruses like influenza in the expansion stage has led to identification of certain patterns that are followed and a gain of knowledge of the immediate body reactions to the infection spread. Other HIV risky behavior has also been identified and this information put to use for HIV behavior detection. This database is then processed to infer the underlying activities that might be encountered during surveillance for them. The four widely used ML methods on the dataset for abnormality identification are: logistic regression, random forest, SVM, and ridge regression classifier. The aim of this analysis is accurate identification of patterns similar to those observed in patients suffering from HIV infection and hence giving an early alarm to the situation at hand.

7.3.4.3 ­Diabetic Retinopathy

Diabetes mellitus or diabetes is a cluster of metabolic disorders in which the patient experiences high blood sugar level over a prolonged period. The cause for this has been identified as either lack of production of insulin hormone by the pancreas or a state in which the body cannot effectively use the produced hormone responsible for maintaining blood sugar level. As per the World Health Organization, there are four major types of diabetes [44]: 1. Type 1: is identified as a deficit in insulin hormone production and requires daily intake of insulin externally. 2. Type 2: results from the body’s ineffective use of insulin. 3. Gestational: hyperglycemia with glucose levels in blood above normal but below in comparison to diabetic patient, occurring during pregnancy. 4. Impaired glucose tolerance and impaired fasting glycemia: are medical conditions in the transition between normal state and diabetes. If left untreated, this condition can result in several acute and chronic complications, including but not limited to diabetic ketoacidosis, hyperosmolar hyperglycemic state, cardiovascular disease, stroke, chronic kidney disease, foot ulcers, and diabetic retinopathy [45]. Diabetic retinopathy (DR) is one of the most commonly occurring diabetic eye diseases. It can be dangerous, as it can cause complete blindness and visual impairment if untreated. Studies indicate it affects more than 80% of long-term (20 years

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or more) diabetic patients [46]. It is due to changes in blood glucose levels in r­etinal blood vessels, which results in abnormal swelling or leakage causing permanent damage to the eye. DR is currently classified into three types: background retinopathy, diabetic maculopathy, and proliferative retinopathy. Essential data using ocular fundus images on retinal pathological changes is obtained: shape, size, appearance of the exudates, hemorrhages, and microaneurysms in human retina signify the level of disease. Conventionally, practitioners and clinicians employ weighted analysis of key features and their location; this process is time consuming and requires high expertise and years of experience. CAD solutions of DR have been proven to possess high efficacy despite the complexity in extraction of significant features. Feature detection using AI schemes, for example: SVM, convolution neural networks, and k-NN classifiers have been developed [47]. Once trained, the computing schemes can swiftly classify features like exudates that resemble optic discs and microaneurysms, which look like blood vessels. Back propagation–based neural network and multilayer neural network were initially developed to identify and classify DR. Graph cut algorithms are used to segment optical disc, invariant moments bifurcated exudate, and nonexudate scans. Most of the earlier models were based on ANN and did not address the overfitting problem primarily in large-scale fundus scans. Consequently, genetic algorithms and other AI-based approaches were developed to address this issue and localize exudates for better feature extraction.

7.3.5 ­Functional Activity Surveillance and Abnormality Detection Sleep disorders and dysregulation affect millions of people globally and contribute to serious health-related problems eventually including cardiovascular, metabolic, and psychiatric disorders [48]. These include arrhythmia, diabetes, stroke, obesity, depression, and other issues. Further, sleep disorders can be classified into more than 90 categories including insomnia, restless leg syndrome, central sleep apnea, REM, narcolepsy, etc. Sleep disorders are assessed and studied at specialized sleep clinics, which employ nocturnal polysomnography or PSG. This encapsulates data from digital signals sourced from: electroencephalography, electrooculography, chin and leg electromyography, electrocardiography, breathing effort, oxygen saturation, and airflow. Polysomnography is currently done by expert technicians using a standardized screen; there is a transition from N1 to N3 and then to REM several times during a night and this is crucial for identifying a variety of sleep-based disorders, as there are associated physiological changes with each transition (Fig. 7.4). Subjectivity, inaccuracy, lack of coherence, and cost have been issues with the manual inspection of the recording. Neural network deep learning–based systems derived from hypnodensity graphs have proven to be 97% accurate in reference to an MSLT gold standard technician. Here, ML produced a fast, reliable, and inexpensive diagnosis to serious medical issues. With the cost of healthcare on the rise, there is a shift from preventive to valuebased care. Usage technology like wearable healthcare devices and at-home testing

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American Academy of Sleep Medicine Classification

Wake stage

NonREM (NREM) sleep stage 1 (N1) N2 N3 REM

(Sleep onset), Slowing of the EEG, disappearance of occipital alpha waves, decreased EMG and slow rolling eye movements

Associated to spindles and K. complexes Dominance of slow, high amplitude waves greater than 30%

Low voltage, desynchronized EEG with occasional saw tooth waves, low muscle tone and REMS

FIG. 7.4 Characterization based on the AASM criteria for sleep.

machinery has created a proactive attitude toward individual health. Apart from live updates and consolidated data about one’s health, these devices can provide potential medical analysis through analysis of the data consolidated with the use of AI-based pattern recognition techniques. Using basic data like heart rate variability and breathing rate, potential heart and respiratory problems can be identified.

7.4 ­Conclusion Given statistics indicate the prevalence of medical expert–based interpretation of data; however, the trend indicates a shift toward AI-based automated expert systems. Drawbacks such as unreliability, inaccuracy due to human error, time consumption, and cost are being tackled by novel methods being developed under the ambit of AI. In the context of image-based data interpretation, human experts limit the scope by adding subjectivity, fatigue, incoherence, and inefficiency whereas AI-based models have proven to be efficacious solutions with cost-effective prospects. This chapter aims to shed light on the emerging trends in the biomedical sector and artificial intelligence and underscore their success in various theoretical and practical studies undertaken currently.

­References [1] D.  Poole, A.  Mackworth, R.  Goebel, Computational Intelligence, Oxford University Press, New York, 1998.

­References

[2] A. Simmons, S. Chappell, Artificial intelligence-definition and practice, IEEE J. Ocean. Eng. 13 (2) (1988) 14–42, https://doi.org/10.1109/48.551. [3] M. Rao, B. Reddy, Comparative analysis of pattern recognition methods: an overview, Indian J. Comput. Sci. Eng. 2 (3) (2018) 385–390. [4] M.  Cohen, D.  Hudson, L.  Mann, J.  van den Bogaerde, N.  Gitlin, Use of pattern-­ recognition techniques to analyze chromatographic data, J. Chromatogr. A 384 (1987) 145–152, https://doi.org/10.1016/s0021-9673(01)94666-7. [5] P. Deedwania, E. Carbajal, Exercise test predictors of ambulatory silent ischemia during daily life in stable angina pectoris, Am. J. Cardiol. 66 (17) (1990) 1151–1156, https://doi. org/10.1016/0002-9149(90)91090-s. [6] G. Gorry, G. Barnett, Experience with a model of sequential diagnosis. Comput. Biomed. Res. 1 (5) (1968) 490–507, https://doi.org/10.1016/0010-4809(68)90016-5. [7] R.  Lindsay, B.  Buchanan, E.  Feigenbaum, J.  Lederberg, DENDRAL: a case study of the first expert system for scientific hypothesis formation, Artif. Intell. 61 (2) (1993) 209–261, https://doi.org/10.1016/0004-3702(93)90068-m. [8] L.  Roa Romero, Mediterranean Conference on Medical and Biological Engineering and Computing, Springer, Cham, Heidelberg, New York, Dordrecht, London, 2013, pp. 161–189. [9] D.  Raeside, W.  Chu, An application of pattern recognition to echocardiography. IEEE Trans. Syst. Man. Cybern. 8 (2) (1978) 81–86, https://doi.org/10.1109/tsmc.1978.4309904. [10] Sonali. B. Maind, Priyanka Wankar, “Research paper on basic of artificial neural network”, Int. J. Recent Innov. Trends Comput. Commun., Vol. 2 Issue. 1, ISSN: 2321-8169, PP: 96–100. [11] F.  Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65 (6) (1958) 386–408, https://doi.org/10.1037/ h0042519. [12] J. Elman, Learning and development in neural networks: the importance of starting small. Cognition 48 (1) (1993) 71–99, https://doi.org/10.1016/0010-0277(93)90058-4. [13] D.  Hudson, M.  Cohen, Neural Networks and Artificial Intelligence for Biomedical Engineering, Institute of Electrical and Electronics Engineers, New York, 2000. [14] T. Mitchell, Machine Learning, McGraw Hill, New York, 2017. [15] E. Alpaydin, Introduction to Machine Learning. Cumberland, The MIT Press, 2016. [16] S.  Banumathi, Predictive analytics concepts in big data- a survey, Int. J. Adv. Res. Comput. Sci. 8 (8) (2017) 27–30, https://doi.org/10.26483/ijarcs.v8i8.4628. [17] K. Zou, A. O’Malley, L. Mauri, Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models, Circulation 115 (5) (2007) 654–657, https://doi. org/10.1161/circulationaha.105.594929. [18] B.  Xue, M.  Zhang, W.  Browne, X.  Yao, A survey on evolutionary computation approaches to feature selection, IEEE Trans. Evol. Comput. 20 (4) (2016) 606–626, https:// doi.org/10.1109/tevc.2015.2504420. [19] D. Veldhuizen, G. Lamont, Multiobjective evolutionary algorithms: analyzing the state-ofthe-art. Evol. Comput. 8 (2) (2000) 125–147, https://doi.org/10.1162/106365600568158. [20] M.  Dorigo, Ant colony optimization, Scholarpedia 2 (3) (2007) 1461, https://doi. org/10.4249/scholarpedia.1461. [21] Genetic algorithm. En.wikipedia.org. https://en.wikipedia.org/wiki/Genetic_algorithm. Published 2018. (Accessed 13 November 2018). [22] A. Watkins, J. Timmis, L. Boggess, Artificial immune recognition system (AIRS): An immune-inspired supervised learning algorithm, Genet. Program. Evolvable Mach. 5 (3) (2004) 291–317, https://doi.org/10.1023/b:genp.0000030197.83685.94.

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CHAPTER 7  AI-Based Diagnostics, Therapeutics and Applications

[23] M.  Sutton, S.  McNeill, J.  Helm, Y.  Chao, Advances in two-dimensional and threedimensional computer vision, Top. Appl. Phys. (2000) 323–372, https://doi. org/10.1007/3-540-48800-6_10. [24] R. Szeliski, Computer Vision, Springer, 2010. [25] K. Doi, Computer-aided diagnosis in medical imaging: historical review, current status and future potential, Comput. Med. Imaging Graph. 31 (4–5) (2007) 198–211, https://doi. org/10.1016/j.compmedimag.2007.02.002. [26] Hughes Z, Hughes Z, Hughes Z. Medical Imaging Types and Modalities—Ultrasound, X-ray, CT, MRI. Ausmed. https://www.ausmed.com/articles/medical-imaging-typesand-modalities/. Published 2018. Accessed 13 November 2018. [27] A. Makropoulos, I. Gousias, C. Ledig, et al., Automatic whole brain MRI segmentation of the developing neonatal brain, IEEE Trans. Med. Imaging 33 (9) (2014) 1818–1831, https://doi.org/10.1109/tmi.2014.2322280. [28] M. Algorri, F. Flores-Mangas, Classification of anatomical structures in MR brain images using fuzzy parameters, IEEE Trans. Biomed. Eng. 51 (9) (2004) 1599–1608, https://doi. org/10.1109/tbme.2004.827532. [29] A.  Amruta, A.  Gole, Y.  Karunakar, A systematic algorithm for 3-D reconstruction of MRI based brain tumors using morphological operators and bicubic interpolation. in: 2nd International Conference on Computer Technology and Development. 2010, 2010, https://doi.org/10.1109/icctd.2010.5645867. [30] W. He, P. Hogg, A. Juette, E. Denton, R. Zwiggelaar, Breast image pre-processing for mammographic tissue segmentation, Comput. Biol. Med. 67 (2015) 61–73, https://doi. org/10.1016/j.compbiomed.2015.10.002. [31] B.  Hela, M.  Hela, H.  Kamel, B.  Sana, M.  Najla, Breast cancer detection: a review on mammograms analysis techniques. in: 10th International multi-conferences on systems, Signals & Devices 2013 (SSD13), 2013, https://doi.org/10.1109/ssd.2013.6563999. [32] J. Saul, R. Berger, P. Albrecht, S. Stein, M. Chen, R. Cohen, Transfer function analysis of the circulation: unique insights into cardiovascular regulation, Am. J. Phys. Heart Circ. Phys. 261 (4) (1991) H1231–H1245, https://doi.org/10.1152/ajpheart.1991.261.4.h1231. [33] R.  Silipo, C.  Marchesi, Artificial neural networks for automatic ECG analysis, IEEE Trans. Signal Process. 46 (5) (1998) 1417–1425, https://doi.org/10.1109/78.668803. [34] D. Benitez, P. Gaydecki, A. Zaidi, A. Fitzpatrick, The use of the Hilbert transform in ECG signal analysis, Comput. Biol. Med. 31 (5) (2001) 399–406, https://doi.org/10.1016/ s0010-4825(01)00009-9. [35] H. Zhang, L.-Q. Zhang, ECG analysis based on PCA and support vector machines. in: International Conference on Neural Networks and Brain, 2005, https://doi.org/10.1109/ icnnb.2005.1614733. [36] S. Osowski, T.H. Linh, ECG beat recognition using fuzzy hybrid neural network, IEEE Trans. Biomed. Eng. 48 (11) (2001) 1265–1271, https://doi.org/10.1109/10.959322. [37] S. Amendolia, G. Cossu, M. Ganadu, B. Golosio, G. Masala, G. Mura, A comparative study of K-nearest neighbour, support vector machine and multi-layer perceptron for thalassemia screening, Chemom. Intell. Lab. Syst. 69 (1–2) (2003) 13–20, https://doi. org/10.1016/s0169-7439(03)00094-7. [38] M. Payandeh, M. Aeinfar, A. Maleki, M. Erfan Zare, A new method for diagnosis and predicting blood disorder and Cancer using artificial intelligence (artificial neural networks), Int. J. Hematol.Oncol. Stem Cell Res. 12 (2009) 25–33. [39] L. Kalia, A. Lang, Parkinson's disease, Lancet 386 (9996) (2015) 896–912, https://doi. org/10.1016/s0140-6736(14)61393-3.

­References

[40] G.  Rizzo, M.  Copetti, S.  Arcuti, D.  Martino, A.  Fontana, G.  Logroscino, Accuracy of clinical diagnosis of Parkinson disease, Neurology 86 (6) (2016) 566–576, https://doi. org/10.1212/wnl.0000000000002350. [41] W.  Gibb, A.  Lees, The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease, J. Neurol. Neurosurg. Psychiatry 51 (6) (1988) 745–752, https://doi. org/10.1136/jnnp.51.6.745. [42] A.  Salarian, H.  Russmann, F.J.G.  Vingerhoets, P.R.  Burkhard, Y.  Blanc, C.  Dehollain, K. Aminian, An ambulatory system to quantify bradykinesia and tremor in parkinson's disease, in: Information Technology Applications in Biomedicine, 2003, 4th International IEEE EMBS Special Topic Conference on, 2003, pp. 35–38. [43] A. Valli, G. Jiji, Parkinsons disease diagnosis using image processing techniques A survey, Int. J. Comput. Sci. Appl. 4 (6) (2014) 57–67, https://doi.org/10.5121/ijcsa.2014.4605. [44] Diabetes.World Health Organization, http://www.who.int/news-room/fact-sheets/detail/ diabetes, 2018. (Accessed 13 November 2018). [45] WHO | Diabetes Web.archive.org. https://web.archive.org/web/20130826174444/http:// www.who.int/mediacentre/factsheets/fs312/en/. Published 2018. (Accessed 13 November 2018). [46] P. Kertes, T. Johnson, Evidence Based Eye Care, Lippincott Williams & Wilkins, 2007. [47] R.  Mansour, Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy, Biomed. Eng. Lett. 8 (1) (2017) 41–57, https://doi.org/10.1007/ s13534-017-0047-y. [48] J. Stephansen, A. Ambati, The Use of Neural Networks in the Analysis of Sleep Stages and the Diagnosis of Narcolepsy, Research gate 2017.

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