- Email: [email protected]
Neural network in hematopoietic malignancies
Clinica Chimica Acta 333 (2003) 195 – 201 www.elsevier.com/locate/clinchim
Neural network in hematopoietic malignancies Gina Zini *, Giuseppe d’Onofrio Researcher Center for Clinical Evaluation of Automated Method in Hematology, Hematology Department, Catholic University of Sacred Heart, Policlinico Gemelli, Laboratorio di Ematologia, L.go A. Gemelli 8, 00168, Rome, Italy
Abstract Background: In the last 30 years, the automatization of the hematology laboratory has led to a high increase in the number of full blood counts (FBC) performed daily as well as a very high degree in the quality assessment in terms of accuracy and precision. Moreover, the produced data are so many that their full interpretation needs an expert; often these new parameters are not exploited. The new challenge of the hematology laboratory consists in the translation of numerical data and new parameters into clinical meaningful information also offering, when possible, a pre-diagnostic guideline, maintaining similar high quality level also in these new aspects. Methods: Since the first development of automated cytochemistry for leukocyte differential count, a new efficient pre-microscopic approach to leukemia diagnosis and classification, was made possible at the time of the automated blood cell count. This original method, used by the Bayer hematological H* series and ADVIA120 analyzers, is based on a light assessment of basic cell properties: volume and peroxidase activity (P) in the Perox channel, which is used to differentiate the main leukocyte types according to the different enzyme content and Nuclear density (ND) in the basophil channel, which is used to count basophils and blast cells according the chromatin pattern and to flag the presence of abnormal or immature cells. The simple observation of P and ND two-dimensional cytograms can be used to include any single case in a separate and distinct diagnostic category correlated to FAB and WHO classifications of hematological malignancies. Moreover, on the RBC map, it is possible to obtain some information about the pathological cluster. The simultaneous observation of these three cytograms and the introduction of a simple and quick score provide objective informations on blast lineage, level of myeloid differentiation, chronic versus acute leukemias. Using a simple visual score system, we have reached in a previous study a pre-diagnostic efficiency in 91% of the analyzed samples. ADVIA120 provides cytogram cell distribution at the end of the analytical processes that include the evaluation of 492 signals, the so-called raw data. The working hypothesis is to create a knowledge-based system, an Artificial Neural Network able to directly handle these raw-data for producing classes of diagnostic probability with a high level of efficiency. The Bayer R&D team in Tarrytown has created an ANN software for connecting a set of input data to output through weighted ‘‘hidden layers’’ (i) assessing the 84 ADVIA120 parameter sets to be used, (ii) defining the interim analysis tool for fitting to standard ‘‘normal archetypes’’, (iii) finding a discriminant function normal vs. pathological. We have collected from 22 Italian hematological centers data from peripheral blood of 1000 patients having mainly hematopoietic disorders at diagnosis. Results and conclusions: The ANN has been trained with labeled samples. Same analysis has been performed for constructing the ‘‘pathological archetypes’’. Finally, we have tested the ANN model with a small selected set of the collected pathological samples. The preliminary encouraging results show the high capability of this ANN of clustering signals according to the pre-defined normal as well as pathological archetypes.
* Corresponding author. E-mail address: [email protected] (G. Zini). 0009-8981/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0009-8981(03)00186-4
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G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
The next big step is to create an application for discriminating different types of anemia, simply using data from patient’s peripheral blood. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Artificial Neural network; Automated cytochemistry; Hematological malignancies; Anemia
1. History In 1943, McCulloch [1], a physician turned physiologist and Walter Pitts, a mathematician, wrote a paper titled ‘‘A Logical calculus of the Ideas Immanent in Nervous Activity’’ in which they assumed how neurons in the brain (Fig. 1) might work and modeled a simple neural network using electrical circuits. They proposed a mathematical model of a neuron, which could perform computations. This artificial neuron, or neurode (some call them neurones), was a simple device, which could receive input from other such devices. The neurode’s output was either a 1 or a 0, reflecting the all-or-none theory of biological neurons. When the total input reached a certain critical level, the neurode would send its output to other neurodes with which it was connected. This method is called threshold logic. In 1949, Donald Hebb, father of Cognitive Psychobiology in the ‘‘The Organization of Behavior’’ pointed out the fact that neural pathways are strengthened each time they are used, an essential behavior in the pathway of the human learning: if two nerves fire at the same time, the connection between them is enhanced. In the 1950s, with computer diffusion, it was finally possible to simulate a hypothetical neural
Fig. 1. Biological neuron scheme.
network: the first step towards this was made by Nathanial Rochester from the IBM research laboratories. Unfortunately, for him, the first attempt to do so failed. In 1960s, developed in the Stanford University was a system based on a Multiple ADAptive LINear Elements (MADALINE) to recognize binary patterns, so that if it was reading streaming bits from a phone line, it could predict the next bit. MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines. The basic element of a neural network is the perceptron. First proposed by Frank Rosenblatt in 1958 at Cornell University, the perceptron has five basic elements: an n-vector input, weights, summing function, threshold device, and an output. Outputs are in the form of 1 and/or + 1. The threshold has a setting, which governs the output based on the summation of input vectors. If the summation falls below the threshold setting, a 1 is the output. If the summation exceeds the threshold setting, + 1 is the output (Fig. 2). In 1972, Kohonen and Anderson developed a network independently of one another, They both used matrix mathematics to describe their ideas: the neurons are supposed to activate a set of outputs instead of just one. The first multilayered network was developed in 1975 (Fig. 3a and b). In 1982, Reilly and Cooper used a ‘‘Hybrid network’’ with multiple layers, each layer using a different problem-solving strategy. In 1986, with multiplelayered neural networks in the news, three independent groups of researchers came up with similar ideas which are now called ‘‘back propagation networks’’: the distribution of pattern recognition errors throughout the network [2]. Hybrid networks used just two layers, these back propagation networks use many. The result is that back propagation networks are ‘‘slow learners’’ needing possibly thousands of iterations to learn.
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
Fig. 2. Artificial Neuron Scheme.
In the human brain, there are at least 10 billion neurons, each connected to about 10,000 other neurons. Each neuron receives electrochemical inputs from other neurons at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate the neuron, it transmits an electrochemical signal along the axon, and passes this signal to the other neurons whose dendrites are attached at any of the axon terminals. These attached neurons may then fire. The point is that a neuron fires only if the total signal received at the cell body exceeds a certain level. The neuron either fires or it does not, there are no different grades of firing. So, our entire brain is composed of these interconnected electrochemical transmitting neurons. From a very large number of extremely simple processing units, the brain manages to perform extremely complex tasks. This is the model on which artificial neural networks are based. Neural networks are used in several applications and its future lies in the development of hardware. Researchers are trying to create what is called a ‘‘silicon compiler’’ to generate a specific type of integrated circuit to faster the training and the applications [3].
197
one error but, on the contrary, also using errors improve its learning capability. One of the first medical area interested in the application of this technology is the cardiopulmonary diagnostics. By comparison of many different models, for instance, a set of physiological data, as age, sex, heart rate, blood pressure, breathing rate, work load, etc. versus pathological models, a patient may have regular checkups in a particular area, increasing the possibility of detecting a disease or dysfunction. Each individual’s physiological data is compared to previous physiological data and/or data of the various generic models. The deviations from the norm are compared to the known causes of deviations for each medical condition. The neural network can learn by studying the different conditions and models, merging them to form a complete conceptual picture, and then diagnose a patient’s condition based upon the models as well as make available a Computer Assisted Medical Diagnosis.
2. Neural network medical applications Neural networks have shown to be good at problems which are easy for a human but difficult for a traditional computer, such as image recognition and predictions, based on past knowledge. One of the most important innovative principle introduced by neural networks is that it works as an adaptive system, that means that it is able to model itself on the studied phenomena. Way by way, it does not block against
Fig. 3. (a) and (b). Scheme of a single Kohen and of a multilayer neural network.
198
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
At Pacific Northwest National Laboratory, four computer based prototypes have been developed using artificial neural networks to assist physicians in medical diagnosis. In the same laboratory has been developed an electronic nose that is composed of a chemical sensing system (such as a spectrometer) and an artificial neural network, which recognizes certain patterns of chemicals. An odor is passed over the
chemical sensor array, these chemicals are then translated into a format that the computer can understand and the artificial neural network identifies the chemical. Several applications are presently available. In the medical field, the target consists in the detection of odors from the body to identify and diagnose problems. Odors in the breath, infected wounds, and body fluids all can indicate problems. Artificial
Fig. 4. Principles used by Bayer hematological analyzers: (a) the Perox channel, (b) the basophilic channel.
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
neural networks have even been used also to detect tuberculosis. Focusing on literature, we have Neural Networks Applications in neurology, rehabilitation and monitoring, medical imaging, genetics, oncology, psychology pharmacology, and toxicology.
3. A pilot application on data from a hematological analyzer: preliminary results In the last 30 years, the automatization of the hematology laboratory has led to a high increase in the number of full blood counts (FBC) performed daily as well as a very high degree in the quality assessment in terms of accuracy and precision. Moreover, the produced data are so many that their full interpretation needs an expert; often, these new parameters are not exploited. The new challenge of the hematology laboratory consists in the translation of numerical data and new parameters into clinical meaningful information also offering, when possible, a pre diagnostic guideline, maintaining similar high-quality level also in these new aspects. Since the first development of automated cytochemistry for leukocyte differential count, a new
199
efficient pre microscopic approach to leukemia diagnosis and classification was made possible at the time of the automated blood cell count. This original method, used by the Bayer hematological H* series and ADVIA120 analyzers, is based on a light assessment of basic cell properties: volume and peroxidase activity (P) in the Perox channel, which is used to differentiate the main leukocyte types according to the different enzyme content and nuclear density (ND) in the basophilic channel, which is used to count basophils and blast cells according the chromatin pattern and to flag the presence of abnormal or immature cells (Fig. 4a and b). The simple observation of P and ND two-dimensional cytograms can be used to include any single case in a separate and distinct diagnostic category correlated to FAB and WHO classifications of hematological malignancies [4,5]. Moreover, on the RBC map, it is possible to obtain some information about the pathological cluster. The simultaneous observation of these three cytograms and the introduction of a simple and quick score provide objective information on blast lineage, level of myeloid differentiation, chronic versus acute leukemia (Fig. 5) [6]. Using a simple visual score system, we have reached in a previous study a pre diagnostic efficiency in 91% of the analyzed samples. ADVIA120 provides cytogram cell distribution at the end of the analytical
Fig. 5. The three cytograms ADVIA 120 representation: (a) Chronic Lymphoid Leukemia; (b) Acute Myeloid Leukemia poorly differentiated.
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G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
processes that include the evaluation of 492 signals, the so-called raw data. The working hypothesis is to create a knowledgebased system (KBS) capable of directly handling these raw-data for producing classes of diagnostic probability with a high level of efficiency. Among the available systems, the Artificial Neural Network is up to now the most advanced KBS able not only of quickly handling a lot of data, but mainly, after trained, of self-learning and so of increasing its knowledge platform. The Bayer R&D team in Tarrytown has created an ANN software for connecting a set of input data to output through weighted ‘‘hidden layers’’ (i) assessing the 84 ADVIA120 parameter sets to be used., (ii) defining the interim analysis tool for fitting to standard ‘‘normal archetypes’’, (iii) finding a discriminant function normal vs. pathological. In Fig. 6, we can observe the representation of the extracted ellipses in the perox channel using the listed hidden parameters
in a sample of a patient with B-Chronic Lymphoid Leukemia. We have collected from 22 Italian hematological centers data from peripheral blood of 1000 patients having mainly hematopoietic disorders at diagnosis. The ANN has been trained with labeled samples. Same analysis has been performed for constructing the ‘‘pathological archetypes’’. Finally, we have tested the ANN model with a small selected set of the collected pathological samples. The preliminary results show the high capability of this ANN of clustering signals according to the predefined normal as well pathological archetypes. Works are in progress. The next big challenge is to create an application for discriminating different types of anemia, simply using data from patient’s peripheral blood. We want to conclude reminding one of McCulloch’s favorite teaching: ‘‘Don’t bite my finger, look where I am pointing’’.
Fig. 6. Representation of extracted ellipses in the Perox channel in a sample of a patient with B-Chronic Lymphoid Leukemia.
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
References [1] McCulloch WS. Embodiments of mind. Cambridge, MA: The M.I.T. Press; 1965. [2] Bose NK, Liang P. Neural network fundamentals with graphics, algorithms and applications. New York: McGraw-Hill; 1996. [3] Web site: The Artificial Neural Networks in Medicine World Map: http://www.phil.gu.se/ANN/annworld.html. [4] d’Onofrio G, Zini G, et al. Diagnostic value of peroxidase and
201
size parameters from a new hematological analyzer. Proceedings XXII Congress of the International Society of Hematology, Milan. Haematologica; 1998. [5] d’Onofrio G, Zini G, et al. Patterns of leukemic cell distribution using four optical hematological analyzers. 24th Congress of the International Society of Haematology, London (UK), Brit J Haematol, abstract, vol. 989. 1992. p. 261. [6] d’Onofrio G, Zini G. Morphology of the blood. Oxford: Butterworth Heinemann; 1998.
Neural network in hematopoietic malignancies Gina Zini *, Giuseppe d’Onofrio Researcher Center for Clinical Evaluation of Automated Method in Hematology, Hematology Department, Catholic University of Sacred Heart, Policlinico Gemelli, Laboratorio di Ematologia, L.go A. Gemelli 8, 00168, Rome, Italy
Abstract Background: In the last 30 years, the automatization of the hematology laboratory has led to a high increase in the number of full blood counts (FBC) performed daily as well as a very high degree in the quality assessment in terms of accuracy and precision. Moreover, the produced data are so many that their full interpretation needs an expert; often these new parameters are not exploited. The new challenge of the hematology laboratory consists in the translation of numerical data and new parameters into clinical meaningful information also offering, when possible, a pre-diagnostic guideline, maintaining similar high quality level also in these new aspects. Methods: Since the first development of automated cytochemistry for leukocyte differential count, a new efficient pre-microscopic approach to leukemia diagnosis and classification, was made possible at the time of the automated blood cell count. This original method, used by the Bayer hematological H* series and ADVIA120 analyzers, is based on a light assessment of basic cell properties: volume and peroxidase activity (P) in the Perox channel, which is used to differentiate the main leukocyte types according to the different enzyme content and Nuclear density (ND) in the basophil channel, which is used to count basophils and blast cells according the chromatin pattern and to flag the presence of abnormal or immature cells. The simple observation of P and ND two-dimensional cytograms can be used to include any single case in a separate and distinct diagnostic category correlated to FAB and WHO classifications of hematological malignancies. Moreover, on the RBC map, it is possible to obtain some information about the pathological cluster. The simultaneous observation of these three cytograms and the introduction of a simple and quick score provide objective informations on blast lineage, level of myeloid differentiation, chronic versus acute leukemias. Using a simple visual score system, we have reached in a previous study a pre-diagnostic efficiency in 91% of the analyzed samples. ADVIA120 provides cytogram cell distribution at the end of the analytical processes that include the evaluation of 492 signals, the so-called raw data. The working hypothesis is to create a knowledge-based system, an Artificial Neural Network able to directly handle these raw-data for producing classes of diagnostic probability with a high level of efficiency. The Bayer R&D team in Tarrytown has created an ANN software for connecting a set of input data to output through weighted ‘‘hidden layers’’ (i) assessing the 84 ADVIA120 parameter sets to be used, (ii) defining the interim analysis tool for fitting to standard ‘‘normal archetypes’’, (iii) finding a discriminant function normal vs. pathological. We have collected from 22 Italian hematological centers data from peripheral blood of 1000 patients having mainly hematopoietic disorders at diagnosis. Results and conclusions: The ANN has been trained with labeled samples. Same analysis has been performed for constructing the ‘‘pathological archetypes’’. Finally, we have tested the ANN model with a small selected set of the collected pathological samples. The preliminary encouraging results show the high capability of this ANN of clustering signals according to the pre-defined normal as well as pathological archetypes.
* Corresponding author. E-mail address: [email protected] (G. Zini). 0009-8981/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0009-8981(03)00186-4
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G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
The next big step is to create an application for discriminating different types of anemia, simply using data from patient’s peripheral blood. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Artificial Neural network; Automated cytochemistry; Hematological malignancies; Anemia
1. History In 1943, McCulloch [1], a physician turned physiologist and Walter Pitts, a mathematician, wrote a paper titled ‘‘A Logical calculus of the Ideas Immanent in Nervous Activity’’ in which they assumed how neurons in the brain (Fig. 1) might work and modeled a simple neural network using electrical circuits. They proposed a mathematical model of a neuron, which could perform computations. This artificial neuron, or neurode (some call them neurones), was a simple device, which could receive input from other such devices. The neurode’s output was either a 1 or a 0, reflecting the all-or-none theory of biological neurons. When the total input reached a certain critical level, the neurode would send its output to other neurodes with which it was connected. This method is called threshold logic. In 1949, Donald Hebb, father of Cognitive Psychobiology in the ‘‘The Organization of Behavior’’ pointed out the fact that neural pathways are strengthened each time they are used, an essential behavior in the pathway of the human learning: if two nerves fire at the same time, the connection between them is enhanced. In the 1950s, with computer diffusion, it was finally possible to simulate a hypothetical neural
Fig. 1. Biological neuron scheme.
network: the first step towards this was made by Nathanial Rochester from the IBM research laboratories. Unfortunately, for him, the first attempt to do so failed. In 1960s, developed in the Stanford University was a system based on a Multiple ADAptive LINear Elements (MADALINE) to recognize binary patterns, so that if it was reading streaming bits from a phone line, it could predict the next bit. MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines. The basic element of a neural network is the perceptron. First proposed by Frank Rosenblatt in 1958 at Cornell University, the perceptron has five basic elements: an n-vector input, weights, summing function, threshold device, and an output. Outputs are in the form of 1 and/or + 1. The threshold has a setting, which governs the output based on the summation of input vectors. If the summation falls below the threshold setting, a 1 is the output. If the summation exceeds the threshold setting, + 1 is the output (Fig. 2). In 1972, Kohonen and Anderson developed a network independently of one another, They both used matrix mathematics to describe their ideas: the neurons are supposed to activate a set of outputs instead of just one. The first multilayered network was developed in 1975 (Fig. 3a and b). In 1982, Reilly and Cooper used a ‘‘Hybrid network’’ with multiple layers, each layer using a different problem-solving strategy. In 1986, with multiplelayered neural networks in the news, three independent groups of researchers came up with similar ideas which are now called ‘‘back propagation networks’’: the distribution of pattern recognition errors throughout the network [2]. Hybrid networks used just two layers, these back propagation networks use many. The result is that back propagation networks are ‘‘slow learners’’ needing possibly thousands of iterations to learn.
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
Fig. 2. Artificial Neuron Scheme.
In the human brain, there are at least 10 billion neurons, each connected to about 10,000 other neurons. Each neuron receives electrochemical inputs from other neurons at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate the neuron, it transmits an electrochemical signal along the axon, and passes this signal to the other neurons whose dendrites are attached at any of the axon terminals. These attached neurons may then fire. The point is that a neuron fires only if the total signal received at the cell body exceeds a certain level. The neuron either fires or it does not, there are no different grades of firing. So, our entire brain is composed of these interconnected electrochemical transmitting neurons. From a very large number of extremely simple processing units, the brain manages to perform extremely complex tasks. This is the model on which artificial neural networks are based. Neural networks are used in several applications and its future lies in the development of hardware. Researchers are trying to create what is called a ‘‘silicon compiler’’ to generate a specific type of integrated circuit to faster the training and the applications [3].
197
one error but, on the contrary, also using errors improve its learning capability. One of the first medical area interested in the application of this technology is the cardiopulmonary diagnostics. By comparison of many different models, for instance, a set of physiological data, as age, sex, heart rate, blood pressure, breathing rate, work load, etc. versus pathological models, a patient may have regular checkups in a particular area, increasing the possibility of detecting a disease or dysfunction. Each individual’s physiological data is compared to previous physiological data and/or data of the various generic models. The deviations from the norm are compared to the known causes of deviations for each medical condition. The neural network can learn by studying the different conditions and models, merging them to form a complete conceptual picture, and then diagnose a patient’s condition based upon the models as well as make available a Computer Assisted Medical Diagnosis.
2. Neural network medical applications Neural networks have shown to be good at problems which are easy for a human but difficult for a traditional computer, such as image recognition and predictions, based on past knowledge. One of the most important innovative principle introduced by neural networks is that it works as an adaptive system, that means that it is able to model itself on the studied phenomena. Way by way, it does not block against
Fig. 3. (a) and (b). Scheme of a single Kohen and of a multilayer neural network.
198
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
At Pacific Northwest National Laboratory, four computer based prototypes have been developed using artificial neural networks to assist physicians in medical diagnosis. In the same laboratory has been developed an electronic nose that is composed of a chemical sensing system (such as a spectrometer) and an artificial neural network, which recognizes certain patterns of chemicals. An odor is passed over the
chemical sensor array, these chemicals are then translated into a format that the computer can understand and the artificial neural network identifies the chemical. Several applications are presently available. In the medical field, the target consists in the detection of odors from the body to identify and diagnose problems. Odors in the breath, infected wounds, and body fluids all can indicate problems. Artificial
Fig. 4. Principles used by Bayer hematological analyzers: (a) the Perox channel, (b) the basophilic channel.
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
neural networks have even been used also to detect tuberculosis. Focusing on literature, we have Neural Networks Applications in neurology, rehabilitation and monitoring, medical imaging, genetics, oncology, psychology pharmacology, and toxicology.
3. A pilot application on data from a hematological analyzer: preliminary results In the last 30 years, the automatization of the hematology laboratory has led to a high increase in the number of full blood counts (FBC) performed daily as well as a very high degree in the quality assessment in terms of accuracy and precision. Moreover, the produced data are so many that their full interpretation needs an expert; often, these new parameters are not exploited. The new challenge of the hematology laboratory consists in the translation of numerical data and new parameters into clinical meaningful information also offering, when possible, a pre diagnostic guideline, maintaining similar high-quality level also in these new aspects. Since the first development of automated cytochemistry for leukocyte differential count, a new
199
efficient pre microscopic approach to leukemia diagnosis and classification was made possible at the time of the automated blood cell count. This original method, used by the Bayer hematological H* series and ADVIA120 analyzers, is based on a light assessment of basic cell properties: volume and peroxidase activity (P) in the Perox channel, which is used to differentiate the main leukocyte types according to the different enzyme content and nuclear density (ND) in the basophilic channel, which is used to count basophils and blast cells according the chromatin pattern and to flag the presence of abnormal or immature cells (Fig. 4a and b). The simple observation of P and ND two-dimensional cytograms can be used to include any single case in a separate and distinct diagnostic category correlated to FAB and WHO classifications of hematological malignancies [4,5]. Moreover, on the RBC map, it is possible to obtain some information about the pathological cluster. The simultaneous observation of these three cytograms and the introduction of a simple and quick score provide objective information on blast lineage, level of myeloid differentiation, chronic versus acute leukemia (Fig. 5) [6]. Using a simple visual score system, we have reached in a previous study a pre diagnostic efficiency in 91% of the analyzed samples. ADVIA120 provides cytogram cell distribution at the end of the analytical
Fig. 5. The three cytograms ADVIA 120 representation: (a) Chronic Lymphoid Leukemia; (b) Acute Myeloid Leukemia poorly differentiated.
200
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
processes that include the evaluation of 492 signals, the so-called raw data. The working hypothesis is to create a knowledgebased system (KBS) capable of directly handling these raw-data for producing classes of diagnostic probability with a high level of efficiency. Among the available systems, the Artificial Neural Network is up to now the most advanced KBS able not only of quickly handling a lot of data, but mainly, after trained, of self-learning and so of increasing its knowledge platform. The Bayer R&D team in Tarrytown has created an ANN software for connecting a set of input data to output through weighted ‘‘hidden layers’’ (i) assessing the 84 ADVIA120 parameter sets to be used., (ii) defining the interim analysis tool for fitting to standard ‘‘normal archetypes’’, (iii) finding a discriminant function normal vs. pathological. In Fig. 6, we can observe the representation of the extracted ellipses in the perox channel using the listed hidden parameters
in a sample of a patient with B-Chronic Lymphoid Leukemia. We have collected from 22 Italian hematological centers data from peripheral blood of 1000 patients having mainly hematopoietic disorders at diagnosis. The ANN has been trained with labeled samples. Same analysis has been performed for constructing the ‘‘pathological archetypes’’. Finally, we have tested the ANN model with a small selected set of the collected pathological samples. The preliminary results show the high capability of this ANN of clustering signals according to the predefined normal as well pathological archetypes. Works are in progress. The next big challenge is to create an application for discriminating different types of anemia, simply using data from patient’s peripheral blood. We want to conclude reminding one of McCulloch’s favorite teaching: ‘‘Don’t bite my finger, look where I am pointing’’.
Fig. 6. Representation of extracted ellipses in the Perox channel in a sample of a patient with B-Chronic Lymphoid Leukemia.
G. Zini, G. d’Onofrio / Clinica Chimica Acta 333 (2003) 195–201
References [1] McCulloch WS. Embodiments of mind. Cambridge, MA: The M.I.T. Press; 1965. [2] Bose NK, Liang P. Neural network fundamentals with graphics, algorithms and applications. New York: McGraw-Hill; 1996. [3] Web site: The Artificial Neural Networks in Medicine World Map: http://www.phil.gu.se/ANN/annworld.html. [4] d’Onofrio G, Zini G, et al. Diagnostic value of peroxidase and
201
size parameters from a new hematological analyzer. Proceedings XXII Congress of the International Society of Hematology, Milan. Haematologica; 1998. [5] d’Onofrio G, Zini G, et al. Patterns of leukemic cell distribution using four optical hematological analyzers. 24th Congress of the International Society of Haematology, London (UK), Brit J Haematol, abstract, vol. 989. 1992. p. 261. [6] d’Onofrio G, Zini G. Morphology of the blood. Oxford: Butterworth Heinemann; 1998.