Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review

Tuberculosis 108 (2018) 1e9

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Tuberculosis journal homepage: http://intl.elsevierhealth.com/journals/tube

REVIEW

Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review Payal Dande*, Purva Samant SVKMs NMIMS School of Pharmacy & Technology Management, Shirpur, Maharashtra, 425405, India

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 March 2017 Received in revised form 28 July 2017 Accepted 15 September 2017

Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with HIV, were observed. Most of the TB deaths can be prevented if it is detected at an early stage. The existing processes of diagnosis like blood tests or sputum tests are not only tedious but also take a long time for analysis and cannot differentiate between different drug resistant stages of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection and gives a detailed description about the utilization of ANN as an efficient diagnostic technique. The paper also provides a critical assessment of ANN and the existing techniques for their diagnosis of TB. Researchers and Practitioners in the field are looking forward to use ANN and other upcoming AI tools such as Fuzzy-logic, genetic algorithms and artificial intelligence simulation as a promising current and future technology tools towards tackling the global menace of Tuberculosis. Latest advancements in the diagnostic field include the combined use of ANN with various other AI tools like the Fuzzy-logic, which has led to an increase in the efficacy and specificity of the diagnostic techniques. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Artificial Neural Networks Artificial intelligence Tuberculosis Diagnosis Neuro fuzzy logic

Contents 1. 2. 3. 4. 5.

6. 7. 8. 9. 10.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Existing methods for TB diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Use of artificial neural networks in medical diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Commonly used ANN terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Basic types of neural networks [64] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5.1. Single layered feed forward neural network [65] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5.2. Multilayered feed forward neural network [65] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5.3. Recurrent neural network [64] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 ANN as a diagnostic tool in TB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Developed approaches for use of ANN in diagnosis of TB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Recent advances in TB detection using ANN, neuro fuzzy logic and genetic algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Future scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

* Corresponding author. Mukesh Patel Technology Park, Babulde, bank of Tapi river, SVKMs NMIMS School of Pharmacy & Technology Management, MumbaiAgra Highway, Shirpur, 425405, Dist. Dhule, Maharashtra, India. E-mail addresses: [email protected] (P. Dande), [email protected] (P. Samant). https://doi.org/10.1016/j.tube.2017.09.006 1472-9792/© 2017 Elsevier Ltd. All rights reserved.

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Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1. Introduction As per a global report by the WHO in the year 2016 around 10.4 million people contracted Tuberculosis [TB]. About 1.8million people died due to TB which also included four lakh HIV patients. 60% of TB cases were reported in six countries including China, India, Indonesia, Pakistan, Nigeria and South Africa. As per statistics only one in five people who were in need of Multi drug resistant TB treatment actually received it in 2015. And only about half of this population was cured of TB [1]. In India about 2.2 million people contract TB each year and approximately 220,000 die from the disease [2]. As per a report by WHO in 2015, around 67% HIV positive patients contracted TB. There were a total of about 995 thousand females and 1850 males who fell ill with TB [1]. Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis [3]. According to a report generated by the WHO on ‘TB burden estimates, notifications and treatment outcomes’, an average of 480,000 HIV negative people died due to TB in the year 2015 [4]. Tuberculosis is still a major health problem in developing countries due to specific social and economic burden. 2. Existing methods for TB diagnosis A number of tests are employed for TB detection like Polymerase chain reaction, nucleic acid magnification test and TB interferon gamma release assay. However all these tests have one of the major drawbacks that they are not specific that is they are ineffective in differentiating between drug resistant TB and normal TB, besides

they require a lot of time for interpretation of results, and involve invasive techniques that are tedious to perform. Some of the main TB diagnostic tests are listed below in Table 1. Current diagnostic techniques such as microscopic sputum examination, TB chest X-ray, skin test and culture are not only time consuming but also have low efficacy rates. This has led to an extensive research for the development of new rapid and accurate diagnostic tools and techniques to achieve higher sensitivity and specificity to control the disease and reduce the death rates. Thus the need to find newer and better methods has led to the discovery of Artificial Neural Network. 3. Use of artificial neural networks in medical diagnosis The term Artificial Intelligence [AI] is used for systems which execute certain tasks that would otherwise require human intervention. Tasks such as decision making, visual perception, speech recognition and translation of languages can be performed using AI systems. Artificial Neural network is one such AI tool that has been extensively studied in the field of diagnosis of various diseases. Dr. Robert H. Nielsen, inventor of first neurocomputer, has defined a neural network as [17]: “ … a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.” An artificial neural network (ANN) draws inspiration from our brain or the biological neural networking system [18]. It is aimed at processing a large amount of data simultaneously. Signals reach the neurons at their axon terminals

Table 1 Main Shortcomings of TB diagnostic tests. Test

Methodology

Interpretations

Shortcomings

References

1. Chest X-ray

X-ray of chest recorded to detect inflammation in the lungs. Injecting small amount of Tuberculin in the lower arm and observing the swelling.

Abnormal shadow visible on Xray

Cannot exclude extra pulmonary TB

[5]

Bigger the raised area of the swelling, more are the chances of being infected by TB

May give false results if person was infected by some other bacteria. Cannot differentiate between latent TB and active TB. Blood sample must be instantly examined, laboratory required, test is for detecting latent TB. In cases of HIV and TB coinfection, TB cannot be detected due to low levels of TB bacteria.

[6,7]

2. TB skin test

3. TB Interferon gamma release assays (IGRAs) 4. Sputum smear test

5. Fluorescent microscopy

6. Culturing bacteria to test

7. Polymerase chain reaction

8. GeneXpert test 9. Nucleic acid amplification test

Mix blood sample with special substances to identify interferon gamma cytokine. A series of special stains are applied to a thin smear of patient's sputum and it is examined under microscope for signs of TB bacteria. Illumination of patients sputum smear with quartz/high pressure mercury lamp Culture the bacteria from biological sample of patient on M.tuberculosis selective media The assay targets the KatG gene having unique sequence in TB bacterium. Identification of DNA present in TB bacteria. Amplification of nucleic acids from biological specimens of suspected patient.

Morphological characteristics identification to detect presence of M.tuberculosis

Morphological characteristics identification to detect presence of M.tuberculosis Detection of presence of bacteria by observing colony characteristics Presence of the Mycobacterium tuberculosis complex will give the test positive. If DNA found, patient if TB positive. If nucleic acids found, patient is TB positive.

[8]

[9,10]

Expensive and time consuming

[11,12]

Time consuming

[13]

Expensive

[14]

Expensive.

[15]

Lower sensitivity for respiratory tract specimens.

[16]

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Fig. 1. Signal transmission between two neurons [20].

through synapses between the dendrites and axon terminals of the neuron [Fig. 1]. When these signals are strong enough to surpass a certain threshold, activation of the neuron takes place and results in the emission of a signal though its axon. This signal might traverse to another synapse and activate other neurons [19]. The artificial neural network [ANN] functions in a similar way and is useful in various fields including medicine, agriculture, engineering and other sciences. Artificial Neural Networks in medical diagnosis is currently an extensively researched area in medicine and is expected to have a wider application in biomedical systems in the forthcoming years. Neural Networks can be considered as an ideal tool in identifying diseases using scans. ANN learns by experience and hence the detailed description of identifying the disease is not needed [21]. Table 2 summarizes the various applications of ANN in the medical diagnosis field.

4. Commonly used ANN terminology Fig. 2 shows working of an artificial neuron and below are the terms associated with it. 1. Neuron: Artificial nodes that process information [59]. 2. Input layer: A neuron receiving information from a source that is outside the neural network is called an input layer [60].

3. Output layer: A neuron containing the neural network's classifications or interpretations is called the output layer [60]. 4. Hidden layer: Neurons found in between the input and output layers which aid in processing the information from the hidden layer [60]. 5. Weights: The weights can be adaptive and considered as a connection determining the strength between neurons that get activated during training and interpretation. The value of the weight indicates the strength of the connection between the two neurons [59]. 6. Threshold: The output of the neuron is 0 or 1 and is determined P by whether the sum of weight*input wjxj is less than or greater than the neuron’s threshold value. The threshold is a parameter of the neuron and just like the weights, it is a real number. In precise algebraic terms [61] P Output¼ 0 if w x threshold P j j Output¼ 1 if wjxj>threshold 7. Training an ANN: training a neural network is a procedure in which the value for each weight is decided such that the combination of these values will result in minimum possible error [62]. 8. Learning: Learning implies the process in which a neuron adapts to changing its input/output due to changes in its governing parameters [63]. The neurons receive data from the input neurons and transfer it

Table 2 Summary of Diagnostic uses of ANN in medical field. Discipline

Application field

Targeted symptom

References

a) Cardiology b) ECG c) Intensive care d) Gastro-enterology e) Pulmonology f) Oncology g) Pediatric h) Neurology i) EEG j) Ophthalmology k) Pathology l) Cytology m) Genetics

Diagnostic, Prognosis Diagnostic Prediction Prediction Diagnostic Diagnostics, prognosis Diagnostics Signal processing, modelling Diagnostic Signal processing and monitoring Diagnostic,prognosis Diagnostic, rescreening Diagnostics

Change in heart enzyme levels Fluctuating ECG signals Changes and interactions of physical, chemical and thermodynamic parameters GIT malfunctioning Pulmonary embolism or nodules Size of tumour, interaction tumour with hormones, palpable lymphatic nodule count

[16e27] [22e25,28e31] [26e29,32e35] [30,36] [31,32,37,38] [33e35,39e41] [36,42] [37e39,43e45] [40,46] [41,42,47,48] [43e47,49e53] [48,49,54,55] [50e52,56e58]

Brain SPECT image Fluctuation in EEG complexes Shape abnormalities of cornea, visual field diagnosis Examination of smears to find defects Genetic pattern, chromosomal arrangement

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Fig. 2. Working of an artificial neuron.

to the hidden layers. Here the data gets multiplied by the “weights” attributed to each layer as shown in Fig. 2. After mathematical processing of the data, the result gets transferred to the proceeding layer neurons. Finally the neurons in the last layer provide the network's calculated output [61]. To explain in layman's terms, TB patients show symptoms ranging from weight loss, chills, fatigue or even worse, coughing out blood. Some of these symptoms are common to other diseases. However the most prominent is persistent coughing that lasts for more than 3 weeks and blood in cough that is found in majority of the TB cases. So while assigning weights this priority is taken into consideration e.g. we can assign weight u1¼6 for persistent cough over three to four weeks, u2¼6 for observing blood in cough and u3 ¼ 3 for fatigue and weight loss. By these parameters the ANN will give output¼1 when persistent cough and blood in cough symptoms are observed and output¼0 when the weights of symptoms is below the threshold value of the ANN.

5. Basic types of neural networks [64] 5.1. Single layered feed forward neural network [65] A neural network in which the source node of the input layer transcends into the output layer but not reverse is known as Single Layered Feed Forward Neural Network as shown in Fig. 3. 5.2. Multilayered feed forward neural network [65] The Multilayered Feed Forward network [Fig. 4] consists of more than one hidden layers called hidden neurons or hidden units. The hidden neurons work in linking the external input neuron with the other neurons in the network. The input from very first source of neuron passes the signal to the next i.e. second layer (first hidden layer). The signals received as output of second layer then act as inputs to the proceeding third layer and it continues till the final

Fig. 3. Single Layered Feed forward Neural Network [66].

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Fig. 4. Multilayer Feed forward Neural Network [70].

output layer. The final layer of output neurons constitutes for the total signal output of all the previous neuronal layers. 5.3. Recurrent neural network [64] It consists of a multilayered feed forward neural network that has one or more feedback loop as shown in Fig. 5. It may be a selffeedback, i.e. output of the neuron is fed back to its own input. The feedback networks sometimes consist of unit delay elements. This makes the feedback loop non-linear. Models of Neural Inter-network based Medical Diagnosis System (NIMD) such as using a k-Nearest Neighbor Classification for Diagnosis pruning are being researched on [67].

radiographic findings, constitutional symptoms and measuring variability caused due to demography using General Regression Neural Network [GRNN]. The methodology used consisted of a distinct pattern of study formed by feeding 21 different input variables [given in Fig. 6] in the GRNN. The parameters were sorted as per the above three criteria. The evaluation was done using a tenfold cross validation approach. The output obtained from the GRNN gave an estimate of the likelihood of prevalence of active TB. The network that was chosen through training and learning process, showed a sensitivity of 100% and a specificity of 72%. Thus authors concluded that chest roentgenograms were useful for the prediction of TB. Thereafter many studies were carried out based on similar lines and showing almost same sensitivity and efficacy.

6. ANN as a diagnostic tool in TB 7. Developed approaches for use of ANN in diagnosis of TB El-Solh et al. carried out a study in 1999, which was amongst the very first reported studies of using ANN in the diagnosis of TB [71]. The objective was to detect the prevalence of TB by checking

A study was carried out by Orhan et al., in 2008 made use of the ANN to predict tuberculosis infection [68]. The objective was to

Fig. 5. Recurrent Neural Network [69].

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Fig. 6. Input Variables for training the GRNN.

detect the prevalence of TB by checking for various typical symptoms of TB using Multilayer Neural Network [MLNN]. Methodology used consisted of a dataset having two classes of blood samples of TB afflicted patients [115 samples] and normal subjects [60 samples]. In all 39 features [given in Table 3] found in these samples were added to train the MLNN [68]. In the above study, three-fold cross-validation methods were used to estimate the performance of the trained neural networks. The output obtained from the MLNN with one hidden layer and MLNN with two hidden layers predicted the prevalence of active TB. The accuracy of MLNN having mono hidden layer was 77% whereas that with two hidden layers was 93.93%.Thus it is evident that the MLNN structure with two hidden layers provided best results for the classification accuracy for the tuberculosis disease diagnosing. Further it was derived that neural networks were a potential diagnostic tool for tuberculosis. Such learning based decision support system can prove to be helpful for the doctors in the diagnosis of disease and planning of suitable drug regime.

Table 3 Input variables for training MLNN. Complaint of cough Ache on chest Dyspnea on exertion Pressure on chest Sound on respiratory tract Leucocyte Thrombocyte Hemoglobin

Body temperature Weakness Rattle in chest Sputum Habit of cigarette Erythrocyte Hematocrit Albumin

Alkaline phosphatase 2 l Amylase Transferase Ck/creatine kinase total Iron (serum) Glucose Calcium Chlorine Creatinine Potassium Total protein Uric acid

Alanine aminotransferase Aspartate amino Bilirubin (totalþ direct) Gamma-glutamiltransferase High Density Lipoprotein Urea and Nitrogen in the blood Cholesterol Lactic dehydrogenase Sodium Triglycerides

8. Recent advances in TB detection using ANN, neuro fuzzy logic and genetic algorithms Shahaboddin Shamshirband et al. in their research on TB diagnosis, carried out in 2014, used the hybrid machine learning approaches [72]. They used 175 samples having twenty features which were classified by incorporating a fuzzy logic controller and artificial immune recognition system. This hybrid approach led to highest classification accuracy for learning rate (a) with a value of 0.8. The method was 99.14% accurate, 87% sensitive and 86.12% specific [72]. Sangheum Hwang et al. in their research in 2016 focused on computer aided diagnostic [CAD] system based on deep Convolutional Neural network for automatic TB screening. The recent deep convolutional neural networks (CNN) developed is viewed as a potential algorithm for various diagnostic purposes. They obtained viable TB screening performance of 96% [73]. ANN has no doubt proved to be a very useful AI diagnostic tool however other AI tools like the Neuro-Fuzzy-logic and genetic algorithms have also shown valuable results. Neuro-fuzzy logic is a system that utilises a learning algorithm inspired from neural network theory to evaluate and determine its parameters (i.e. the fuzzy rules and fuzzy sets) by processing the fed data samples. It works on the “If … And … Then” rule. It primarily involves four steps [74] 1. To fuzzify the input variables e This includes transforming the TB input variables into fuzzy forms that are attributed with a degree of membership in each of the fuzzy sets. 2. To establish the Fuzzy rule e The IF parts are the antecedents and the THEN parts are the consequents in the IF-THEN fuzzy rule. These are decided with the help of experts who analyse the effect of the input variables on TB diagnosis. 3. Analysis of the Fuzzy rule application e This component mainly functions in analysing the output after the fuzzy rule has been applied to the input variables. 4. To defuzzify the output e This involves translating the fuzzified output into values that can be read and interpreted by the medical experts. An additional AI tool is the Genetic algorithm [GA]. In the GA,

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Table 4 Mobile Applications Using AI for diagnosis of TB. Name of the app

Characteristics

1. xRapid [80]

The application xRapid-Lab checks parasites and red platelets in Plasmodium colonies by using computerized imaging. An iPhone can be joined to a magnifying lens and the xRapid-Lab application captures and analyses pictures from thin spread blood slides. The application is able to detect the type of parasite, the parasite proliferation stages and the total red platelets (RBCs) count in less than two minutes. CRyPTIC is an application that trains its artificial intelligence (AI) to perceive drug resistance by providing it with enormous data of genomes that are marked as drug resistant. The product not only helps in perceiving the diverse TB genomes but also suggest the line of treatment with the proper medications. It permits individuals to enter their side effects into the Babylon application and get responses based on their queries. The application scrutinizes a substantial database of manifestations and diseases and using its Artificial intelligence technology, it predicts the disease. Transforms an iPhone into versatile DNA research center. It functions as a hand-held PCR (Polymerase Chain Reaction) Thermocycler that works by making almost a billion duplicates ofDNA of the pathogen found in patient's blood samples and labels them with fluorescent color. iPhone's camera can then distinguish the pathogen by its color, while application recognizes the type of infection observed in test sample.

2. CRyPTIC [81,82]

3. Babylon [83] 4. Biomeme System [84]

the input variables are called ‘Chromosomes’. Each chromosome is allotted a “fitness value” depending on the extent of impact of the variable on the diagnosis of the disease. The chromosomes with highest fitness values are selected as “Parents” and are allowed to mate. They produce candidates with highest fitness function. GA is usually used to optimise the input variables to be selected for use in the ANN and Neurofuzzy logic [74]. AI is a promising tool for the diagnosis of TB however the major drawback is that it requires a trained and qualified person for its implementation. As all activities of setting up a neural network to training it and obtaining output would be a tedious job. Besides the use of statistical tool for the detection of a disease is still a matter of concern. 9. Conclusion Existing tuberculosis diagnostic tests like blood smear testing or sputum testing involves tedious procedures which are not only expensive but also take a longer time for interpretation of results. WHO in its recommendation of 2011 issued a warning against the use of such “substandard tests with unreliable results” for diagnosis of active TB [75]. In fact the WHO committee has recommended banning the inaccurate and unapproved blood tests method for TB that is still being used in most of the countries. Instead it has suggested performing molecular or microbiological tests that are more accurate. By the use of advanced technology in the form of Artificial Intelligence tools, the diagnosis process not only becomes comparatively noninvasive but also aids in obtaining faster results. Similarly the other techniques like the fuzzy-logic; Genetic algorithms and simulation software in coalition with the ANN are found to give much impressive results with better specificity and accuracy for diagnosis. Such techniques with AI will help in augmenting the quality of healthcare services provided by medical experts. Thus AI tools can be viewed as a potential diagnostic tool for not just Tuberculosis but many such life threatening diseases. 10. Future scope The differentiation and detection of Multi Drug Resistant TB, Extensive Drug Resistant TB and Total Drug Resistant TB is time consuming. Diagnosis of tuberculosis and resistant cases needs the development of much faster, efficient, patient friendly and cost effective techniques with more precision. Knowledge of biomarkers helps to a large extent in tuberculosis therapy and diagnosis. Therefore the evaluation of biomarkers is the top most priority in current research. Adhesins are cellular structures on the bacterium which help in proliferation by adhering to the host cell Thus Adhesins serve as an important biomarker in diagnosis of Tuberculosis [76]. The Artificial Neural Network technology can be used not only for symptomatic diagnosis but also for determination of

biomarkers like these Adhesins [76] on Mycobacterium tuberculosis that will prove useful in the near future. Research using Artificial Neural Networks for Optical Imaging of Fluorescent Biomarkers is already in progress [77]. Besides new data mining techniques like Ensemble classifiers, clustering, numeric prediction etc. are used to confirm the presence of the disease by extracting hidden information from a large set of database. Ensemble classifier is one of the data classification techniques related to data mining, in which decision of multiple base classifiers is combined for accurate prediction of the presence or absence of abnormality [78,79]. Artificial Intelligence tools can also be also used to design various mobile phone applications that help in diagnosis on the go. The [Table 4] below gives a list of such apps developed in the recent years. It can further be improvised to make the diagnosis more economic, prompt and accurate. Funding This review did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interest The authors have no conflicts of interest. Acknowledgement We would like thank the faculty members of SVKM's NMIMS School of Pharmacy & Technology Management, Shirpur, India for their support and encouragement throughout the review process. References [1] WHO global tuberculosis report. 2016 [Cited Sept. 2016]. Available at: http:// www.who.int/tb/publications/global_report/en/. [2] TB India 2016 revised national TB control programme annual status report”, New Delhi. 2016 [Cited Sept 2016] Available at: www.tbcindia.nic.in. [3] Burstein Z. A network model of developmental gene hierarchy. J Theor Biol 1995;174(1):1e11. [4] TB burden estimates, notifications and treatment outcomes: for individual countries and territories, WHO regions and the world. [Cited Feb 2017]. Available at: http://www.who.int/tb/publications/global_report/en/. [5] Tuberculosis (TB): diagnosis- national jewish health. [Cited Jun. 2016]. Available at : www.nationaljewish.org/healthinfo/COnditions/tb/diagnosis. [6] TB testing & diagnosis. [Cited Sept 2016]. Available at: www.cdc.gov/tb/topic/ testing/. [7] Tuberculin skin test. [Cited Jun. 2016]. Available at: http://www.cigna.com/ individualandfamilies/health-and-well-being/hw8/medical-tests/tuberculinskin-test-hw203560.html. [8] Guidelines for intensified case finding and isoniazid preventative therapy for people living with HIV in resource constrained settings Geneva, WHO.c2011[Cited Jun. 2016]. Available at : http://www.who.int/tb/publications/2011/. [9] Sputum culture [Cited Jul 2016]. Available at, www.webmd.com/lung/ sputum-culture. [10] Sputum gram stain e overview, University of Maryland Medical Center. [Cited

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