Computer aided diagnostic problem solving: Identification of peripheral nerve disorders

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www.sciencedirect.com IRBM 34 (2013) 244–251

Original article

Computer aided diagnostic problem solving: Identification of peripheral nerve disorders R. Kunhimangalam a,∗ , S. Ovallath b , P.K. Joseph a a b

National Institute of Technology, NIT Calicut (PO), Kozhikode, 673601 Kerala, India Department of Neurology, Kannur Medical College, Anjarakandy (PO), Kerala, India

Received 2 December 2012; received in revised form 6 April 2013; accepted 11 April 2013 Available online 18 May 2013

Abstract Aim. – The aim was to design and develop a decision support system with a graphical user interface for the prediction of the case of peripheral nerve disorder and to build a classifier using artificial neural networks that can distinguish between carpal tunnel syndrome, neuropathy and normal peripheral nerve conduction. Materials and methods. – The data used were the Nerve Conduction Study data obtained from Kannur Medical College, India. A recurrent neural network and a two-layer feed forward network trained with scaled conjugate gradient back-propagation algorithm were implemented and results were compared. Results. – Both the networks provided fast convergence and good performance, accuracy being 98.6% and 97.4% for the recurrent neural network and the feed forward networks respectively, the confusion matrix in each case indicated only a few misclassifications. The developed decision support system also gave accurate results in agreement with the specialist’s diagnosis and was also useful in storing and viewing the results. Discussions. – In the field of medicine, programs are being developed that aids in diagnostic decision making by emulating human intelligence such as logical thinking, decision making, learning, etc. The system developed proves useful in combination with other systems in providing diagnostic and predictive medical opinions. It was not meant to replace the specialist, yet it can be used to assist a general practitioner or specialist in diagnosing and predicting patient’s condition. Conclusions. – The study proves that artificial neural networks are indeed of value in combination with other systems in providing diagnostic and predictive medical opinions. But the major drawback of these studies, which makes use of the nerve conduction study data are the inherent shortcomings of the interpretation of the results, which include lack of standardization and absence of population-based reference intervals. © 2013 Elsevier Masson SAS. All rights reserved.

1. Introduction Neurological disorders affecting the peripheral nervous system consists of a spectrum of disorders which include more than 100 peripheral nerve disorders out of which carpal tunnel syndrome (CTS) and symmetrical peripheral neuropathy predominate. The contributions of electrophysiological studies to the understanding and diagnosis of peripheral nerve disorders have been reviewed extensively [1,2]. CTS which is a common peripheral nerve disorder is an entrapment type neuropathy caused as the result of the entrapment of the median nerve

∗

Corresponding author. E-mail addresses: [email protected], [email protected] (R. Kunhimangalam). 1959-0318/$ – see front matter © 2013 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.irbm.2013.04.003

passing through the carpal tunnel [3]. The tests for the diagnosis include electromyography (EMG) and the nerve conduction study (NCS), wrist X-rays should also be done to rule out other problems such as wrist arthritis. However, NCS remains the gold standard for the confirmation of the diagnosis of CTS [4,5]. Peripheral neuropathy refers to the impairment of the nerves of the peripheral nervous system, commonly induced either by diseases or trauma to the nerve or as secondary-effects of systemic illness. Different types of peripheral neuropathy have been described, each with its own characteristic set of symptoms, developmental pattern and medical prognosis. The impaired functions and the symptoms depend on the types of nerves that are damaged viz; the motor, sensory or autonomic. Depending on the patient’s condition may be described as predominantly motor neuropathy, predominantly sensory neuropathy, sensory-motor neuropathy, autonomic neuropathy, etc. The differentiation is

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best accomplished using NCS and EMG [6]. These tests can confirm the presence of neuropathy and also whether it is motor, sensory or both and also the pathophysiology, i.e. whether it is due to demyelination or axonal loss. NCS is a crucial component of the electro diagnostic evaluation, which provides valuable quantitative and qualitative insights into neuromuscular function, particularly, the ability of electrical conduction of the motor and sensory nerves of the human body [7–9]. It must be performed with careful attention to the technique and must be interpreted in the clinical context. Mathematical science and engineering precepts have been widely employed in the field of medicine [10]. There has evolved a number of techniques, which aid in the medical diagnosis, neural networks (NN) being one among them [11]. They have become well established as executable, multipurpose, robust computational methodologies with firm theoretic back up and with strong potential to be effective in any discipline, especially medicine [12]. Whatever be the computer language or the underlying methodology the clinical decision support systems deals with medical data and is based on the knowledge of medicine necessary to interpret such data. In general they are employed in determining the nature of the disease but they may further be programmed to even formulate and develop a plan for reaching a diagnosis or administering therapy appropriate for a specific disease or patient [13]. Any computer program designed to assist in making a clinical decision can be called a clinical decision support system (CDSS) [13] or a medical decision support system [14]. There are mainly two types of CDSS: the first type is knowledge based which consists of three parts, the knowledge base, inference engine, and a user interface which forms a mechanism for the communication between man and machine. The knowledge base contains the rules and associations of the compiled data, which are mostly in the form of IF-THEN rules. The second type is the non knowledge-based CDSS’s that do not use a knowledge base but use a form of artificial intelligence called machine learning, which allow computers to learn from past experiences and/or find patterns in clinical data. Two types of non knowledge-based systems are artificial neural networks (ANN) and genetic algorithms. In this paper, we have designed and developed knowledge based CDSS using MATLAB with a GUI which consists of a simple text oriented display for the prediction of the case of peripheral nerve disorder when provided with the NCS data of the patient. A non knowledge-based system, using ANN in the form of a classifier that can distinguish between CTS, neuropathy and normal peripheral nerve conduction was also developed. For this, a recurrent neural network (RNN) and a twolayer feed forward network (FFN) trained with scaled conjugate gradient (SCG) back-propagation algorithm was implemented. 2. Methods 2.1. Description of database used in ANN classification In our study, we have used the electronic medical records of Kannur Medical College, Kerala and selected the NCS data of 254 patients out of which 90 were normal cases, i.e. those who had normal NCS values and had no electrophysiological

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evidence of CTS or neuropathy, 100 were patients suffering from CTS and the remaining were having neuropathic symptoms. The NCS was performed using the standard techniques with surface electrode recording on both hands of each subject using constant current stimulator. The ethical committee approval was obtained. The following criteria were applied for identifying the data for ANN classification [4]: For CTS • median motor latency greater than 4.4 ms, median sensory latency greater than 3.84 ms, median velocities less than 50 m/s, normal ulnar motor values (latency: 2.59 ± .39 ms, velocity: 58.7 ± 5.1 m/s) and normal ulnar sensory values (latency: 2.54 ± .29ms, velocity: 54.8 ± 5.3 m/s). For neuropathy • ulnar motor latency will be greater than 3 ms, the ulnar sensory latency will be greater than 3.23 ms, the median motor latency and median sensory latency same as the CTS case. Ulnar and median velocities are lesser than 50 m/s. The slowing down of the nerve conduction velocity (NCV) and prolonged distal latencies normally suggests there is damage to the myelin while a reduction in the strength of impulses is a sign of axonal degeneration. Slowing of the motor and sensory latencies of the median nerve is an indication of the focal compression of the median nerve at the wrist, which is an indication for CTS [4]. The slowing of all nerve conductions in more than one limb indicates generalized diseased nerves, i.e. it indicates generalized peripheral neuropathy [15,16]. Assessment of the peripheral neuropathy using NCS can direct a physician towards the appropriate autoimmune disorder. Typically, demyelinating neuropathies demonstrate slow nerve conduction velocities (NCV), often with reduced amplitudes of sensory/motor nerve conduction and prolonged distal latencies. By contrast, axonal neuropathies typically demonstrate normal NCVs with low amplitudes of sensory/motor nerve conduction. Neuropathies may also have mixed EMG/NCS results and exhibit features of both demyelination and axonal loss [4]. A typical NCS report is shown in Table 1, which shows the data for a normal person. 2.2. Developing the program and building the graphical user interface (GUI) for diagnostic prediction Computer aided interpretation of medical data is widespread but a lot of physicians are reluctant in relying on the computer, because the advice is delivered by a computer program and it is never foolproof. Over the last decades, a wide range of computer systems has been developed in the area of medicine for decision support systems, they include computer tools for patient specific consultations e.g. expert diagnostic systems designed to provide differential diagnosis or expert advice. The algorithms used in these types of decision support systems vary substantially but in general such systems depend upon the knowledge and information that are contained in the system. Such systems should

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Table 1 Values of nerve conduction study of a normal person. Site Motor Nerve Conduction Study Median, L Wrist Elbow Median, R Wrist Elbow Ulnar, R Wrist Elbow Ulnar, L Wrist Elbow Sensory Nerve Conduction Study Median, L Wrist Median, R Wrist Ulnar, R Wrist Ulnar, L Wrist

Latency (ms)

Amplitude

Distance (mm)

Interval (ms)

NCV (m/s)

3.85 7.88

9.25 mV 10 mV

240

3.85 4.03

59.5

3.76 8.05

8.25 mV 8.5 mV

250

3.76 4.29

58.2

2.68 6.5

5.7 mV 6.25 mV

220

2.68 3.82

57.6

2.67 6.7

6 mV 6.5 mV

225

2.67 4.03

55.83

2.88

28.6 ␮V

140

2.88

48.6

2.75

32.6 ␮V

140

2.75

51

2.25

25.4 ␮V

120

2.25

53.33

2.29

20.3 ␮V

120

2.29

52.4

NCV: nerve conduction velocity.

ideally be able to keep up with the human decision making process. The decision support system developed consists of three main parts: the input, the rule set and the output. Diagnosis of a disease is done by a specialist using a set of rules and the designed system involves the collection of these rules and evaluation of the rule base for a given set of inputs. For developing diagnostic tool for CTS and neuropathy, data is required that is capable of representing the diseases. By consulting the specialist and by analysing the data of the patients, eight NCS value were finalized as the inputs for diagnosis viz. motor median latency, motor median NCV, motor ulnar latency, motor ulnar NCV, sensory median latency, sensory median NCV, sensory ulnar latency and sensory ulnar NCV. The input is given on to the graphical user interface, the values are checked and compared to the rule set and finally the output is obtained as the result. The aim is to develop a MATLAB GUI system, which models the reasoning process of the consultants in the particular medical scenario under consideration. The rule-based decision making program developed utilises an approach where all the knowledge, information and the concerned data is contained in a group or set of rules. The rule base developed consisted of IF-THEN-ELSE rules, which were formulated using the same criteria applied for identifying the data for ANN classification. Every rule contains multiple hypotheses and conclusions, which represent the logical, thought processes of the specialists. The given sets of rules are activated when the information or the data is input into the user interface. The knowledge base can be easily modified or changed. The proposed system reduces the diagnosis time of a physician and additionally increases the accuracy of the diagnosis. The proposed system is not only used for diagnosis, but also used to store and read the results of the diagnosis for future reference. The user has to enter the NCS values into

the interface together with the patient details and when clicked on the GET RESULT button the diagnosis is output by the program. The program can distinguish between and give the result as “normal”, “suggestive of bilateral CTS”, “suggestive of left CTS”, “suggestive of right CTS”, “predominantly motor neuropathy”, “predominantly sensory neuropathy”, “sensory-motor neuropathy”, or “autonomic neuropathy”. The program has also provisions for storing the result on a spreadsheet by clicking on the SAVE RESULT button. This stored data can be easily retrieved for further reference as and when needed. Provision is also provided in the interface for the doctor or the technician to include their comments for each patient which can also be stored. Involvement of the distal part of the peripheral nerve alone is included in this software program. The program can be further expanded to include the involvement of the proximal part of the nerves by including values of stimulation at the elbow level and erb’s point in the upper limb. 2.3. Training of the artificial neural network ANN is a mathematical/computational model inspired by the structural and functional aspects of the biological neural networks. It consists of nodes called neurons and weighted connections that transmit signals between the neurons in a forward or looped fashion; they process the information using a connectionist approach to computing. The FFN can approximate a spatially finite function with a large set of hidden nodes by operating on the input space. The basic difference found in the RNN is that they operate not only on an input space but also on an internal state space, which represents some information on what already has been processed by the network. The difference in the operating principle of the two can be understood from Fig. 1.

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Fig. 1. (a) A schematic representation showing the difference in the basic principle of operation of the recurrent neural networks and feed forward networks. (b) Feed forward network implementation in MATLAB. (c) Recurrent neural network implementation in MATLAB.

In the case of RNN every time it receives a pattern, the unit computes its activation just like a feed forward network. But its net input will contain a term reflecting the state of the network before the pattern was seen. In the subsequent patterns, the hidden and output states will be a function of everything the network has seen so far, i.e. the network behaviour of RNN is based on its history [17]. Consider a two-layered network, i.e. a network with two layers of nodes; an input layer, a hidden or state layer, and an output layer. In a feed forward network, the input vector, x, is propagated through a weight layer, U and we have the following Eqs. 1–4: yk (t) = f (netk (t)) netk (t) =

n 

xi (t) uki + θk

(1)

yk (t) = f (netk (t)) (2)

i





yj (t) = g netj (t) netj (t) =

l 

yk (t) wjk + θj

through it during the learning phase. At the entrance of each artificial neuron, which is the basic building block of every ANN the inputs are weighted, i.e. every input signal is multiplied with an individual weight. In the succeeding section is the summing function that adds together all the weighted inputs and bias. At the exit the sum of previously weighted inputs and biases passes through the activation function also called the transfer function to get the final output. Fig. 2 shows the ANN basic structure. In a simple RNN, the input vector is similarly propagated through a weight layer, and is also combined with the previous state activation through an additional recurrent weight layer, V, [18] and we have Eqs. 5 and 6.

netk (t) = (3) (4)

k

with the following index variables: n for the input nodes, k for the hidden, j for the output nodes, θ’s are the biases and f and g are output functions. W is the set of weights in the output layer and y is the output vector. It consists of three main layers: the input layer; which receives data, i.e. clinical findings, the output layer which gives the results and hidden layer that processes the data and arrives at the conclusion. The structure of the network changes based on the external or internal information that flows

n  i

xi (t) uki +

(5) l 

ym (t − 1) vkm + θk

(6)

m

where l is the number of state nodes. Eqs. 3 and 4 are applicable for the output layer of the RNN also. The general neural network design process consists of the following steps: the collection of data, the creation of the network, its configuration, the initialization of the weights and biases, the training of the network, the validation of the network and finally using the network [19]. MATLAB software package (MATLAB version 7.9.0 with neural networks toolbox) was used for implementation of the classifiers using neural networks. The NCS data collected was separated into inputs and targets. The significant features were identified from the data and they acts as the inputs to the neural network. Eight inputs were identified which are motor median

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Fig. 2. The basic structure of artificial neural networks (ANN). Table 2 The attributes of the nerve conduction study datasets. Attribute No.

Attribute description

Attribute range

Mean

Standard deviation

1 2 3 4 5 6 7 8

Motor median latency (ms) Motor median nerve conduction velocity (m/s) Motor ulnar latency (ms) Motor ulnar nerve conduction velocity (m/s) Sensory median latency (ms) Sensory median nerve conduction velocity (m/s) Sensory ulnar latency (ms) Sensory ulnar nerve conduction velocity (m/s)

2–9 30–65 2–9 30–65 2–9 30–65 2–9 30–65

4.27 52.35 2.87 53.76 3.6 49.8 3.3 52.76

1.32 8.38 0.69 8.9 1.25 5.79 0.633 6.78

latency, motor median NCV, motor ulnar latency, motor ulnar NCV, sensory median latency, sensory median NCV, sensory ulnar latency, sensory ulnar NCV. The attributes of the datasets are given in Table 2. The targets for the neural network were the logical indices of the disease samples. The normal samples were identified with a 1 0 0, the CTS with a 0 1 0 and the neuropathy with 0 0 1. The samples were divided into training, validation and test sets. The training set teaches the network and training continues as long as the network continues improving on the validation set. The training stops automatically when generalization stops improving which is indicated by an increase in the Mean Square Error (MSE) of the validation samples. MSE is the average squared difference between the outputs and targets, lower values are better, zero means no error. The error goal in training was fixed as E < 0.02 and the termination criterion was set at 1000 epochs. A sigmoid transfer function was used. With the error goal and using trial and error method the number of neurons was selected as 15 for the Scaled Conjugate Gradient (SCG) algorithm. A 2hidden layer feed forward neural network with a 15 hidden layer

neurons as shown in Fig. 3 was build, created and trained, i.e. with 8-15-3-3 nodes. The trained neural network was then tested with the testing samples, which were partitioned from the main dataset. The testing data was not used in training and hence provides a completely independent measure of network accuracy. This gives us a sense of how well the network performs when it is tested with new data. 2.3.1. Scaled Conjugate Gradient algorithm The term learning in a neural network implies the process of minimizing a global error function, E. It is a multivariate function that depends on the weights in the network and minimization is a local iterative process in which the approximation to the function, in a neighbourhood of the current point in the weight space, is minimized. The SCG algorithm denotes the quadratic approximation to the error E in a neighbourhood of a point w by: 1 Eqw (y) = E (w) + E (w)T y + yT E (w)y 2

Fig. 3. The developed neural network consisting of eight inputs, 15 neurons in the hidden layer and three outputs.

(7)

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249

Fig. 4. The graphical user interface of the MATLAB program developed showing the results for a patient with bilateral carpal tunnel syndrome (CTS).

In order to determine the minimum to Eqw [y] its critical points must be found. The critical points are the solution to the linear system defined by Moller [20].  (y) = E (w)y + E (w) = 0 Eqw

(8)

SCG belongs to the class of Conjugate Gradient Methods, showing super linear convergence on most problems. The algorithm is faster than other second order algorithms since it uses a step size scaling mechanism and the time consuming line-search per learning algorithm is avoided. 3. Results

The program gave accurate results, which were in agreement with the specialist’s diagnosis. The user can input the NCS values into the interface and the result could be obtained. Whenever the data entered was not within the designated limits for the disorders the program gave the result as CHECK THE DATA ENTERED. The program developed can identify normal cases, bilateral CTS, left or right CTS and the different neuropathies. At the click of the button to save the result, the full details get stored on to a spreadsheet. This data can be retrieved for future use. Using the decision support system we tested the 95 NCS data, which had normal, CTS and Neuropathy cases. The accuracy of the system was thus found out with the consultation of the expert, the system showed a fairly good accuracy of 95.7%. Also the sensitivity, specificity positive predictive value and the negative predictive value is shown in Table 3. It is seen that it shows high sensitivity and specificity for all the three cases.

3.1. Performance of the decision support system with graphical user interface for diagnosis of peripheral nervous disorders:

3.2. Training using ANN

The Graphical User Interface developed in MATLAB and the results when the data for a patient with bilateral CTS has been entered has been shown in Fig. 4.

The formulated RNN was able to classify the disease with an accuracy of 98.6% and the FFN was able to achieve an accuracy of 97.4%. The sensitivity, specificity and the precision were also

Table 3 The values of the statistical parameters; sensitivity, specificity, positive predictive value and negative predictive value.

Normal CTS Neuropathy

Sensitivity (%)

Specificity (%)

Positive predictive value (%)

Negative predictive value (%)

97 96.5 93.5

96.7 97 98.4

94.2 93.3 97

98.3 97 97

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Table 4 Comparison of performance measures of the developed neural network. Sensitivity measures the proportion of the correctly identified positives, specificity measures the proportion of correctly identified negatives, precision the percentage of correct predictions and accuracy measures the degree of closeness to the true value. Measure

Formula

RNN

FFN

Precision (%) Sensitivity (%) Specificity (%) Accuracy (%) CPU time (s) Epochs

TP/(TP + FP) TP/(TP + FN) TN/(TN + FP)

99.6 98.3 99.2 98.6 10 16

99.1 96.9 98.3 97.4 12 20

RNN: recurrent neural network; FFN: feed forward network; TP: true positive; FP: false positive; TN: true negative; FN: false negative.

found to be higher for the RNN. The performance indices of the training algorithm are given in Table 4. The sensitivity and specificity also known in statistics as classification functions are statistical measures of the performance of a binary classification test [21]. Sensitivity or the true positive rate measures the proportion of actual positives, which are correctly identified as such, i.e. it relates to the test’s ability to identify positive results (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity or the true negative rate measures the proportion of negatives, which are correctly identified, i.e. the ability of the test to identify negative results. (e.g. the percentage of healthy people who are correctly identified as not having the disease). These measures require a decision rule (or positivity threshold) for classifying the test results as either positive or negative. Precision gives the percentage of correct predictions. These quantities are given in Table 4 and were obtained by training the binary set which ROC Curves 1 0.9 0.8 FFN RNN

0.7

Sensitivity

0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.1

0.2

0.3

0.4 0.5 1-Specificity

0.6

0.7

0.8

0.9

Fig. 5. Receiver operating characteristics. Plot of sensitivity (true positive rate) vs. 1- specificity (false positive rate). The solid line represents the curve for the feed forward network and the dotted line represents the curve for the recurrent neural network.

consisted of the CTS data and the neuropathy data. The ROC curves given in Fig. 5 can be used to describe the performance of the classifier. In an ROC curve, every possible decision threshold is considered. It is created by plotting the true positive rate against the false positive rate. Each point on the curve represents the sensitivity and false positive rate at a different decision threshold. A good test is characterized by a rapid rise in sensitivity (true positive rate) with very little rise in 1-specificity (false positive rate) until the sensitivity becomes very high. From the curves it is clear that the RNN gives a better performance. So such a network when given a new data is correctly able to identify the disease condition. 4. Discussion Many controlled clinical trials evaluating the effects of such computer-assisted systems have been done earlier [22,23]. In our recent work [24], we had shown how artificial neural networks could be used successfully for the diagnosis of peripheral nerve disorders such as CTS and neuropathy. It has been suggested that such systems can be used for drug dosing, preventive care and other aspects of medical care but cannot be used for diagnosis convincingly [25]. One of the first rule-based expert systems in the clinical setting was MYCIN developed at Stanford University by Dr. Edward Shortliffe in the 1970s [26], MYCIN was based up on about 600 rules and was used in the identification of the type of bacteria causing an infection. Later Internist-1 [27], an experimental computer program which was helpful in making multiple and complete diagnoses in internal medicine was developed. The Stanford AI group later developed ONCOCIN, another rule-based expert system coded in Lisp in the early 1980s and was used to assist the physicians in the treatment of cancer patients. DiagnosisPro is another medical expert system, which provided exhaustive diagnostic possibilities for 11,000 diseases and 30,000 findings. After a survey of the various expert systems, the authors have not yet found a tool for the prediction of the peripheral nerve disorders from the NCV data. The program developed in our study with a graphical user interface built in MATLAB was able to correctly diagnose the disease from the NCS data entered. The software developed was not meant to replace the specialist, yet it can be used to assist a general practitioner or specialist in diagnosing and predicting patient’s condition. The rules given in the computer program actually replicates the type of decision making done in the trained mind of a specialist. Employing the use of computer aided techniques in medical applications could reduce the cost, time, human expertise and medical error [28]. The study also aims at pointing out the usefulness of the ANN in disease diagnosis. ANN which are computer-based, self-adaptive models evolved in the 1960s and attained great popularity in the mid 1980s. ANNs have been widely used for tasks such as pattern classification, time series forecasting, function approximation, etc. In the field of medical diagnosis application it acts as a powerful tool to help doctors to analyze and model clinical data and make use of them for a number of medical applications [29–31]. One of the most important applications to medicine is classification problems; that is the task of assigning the patient to one of a small set

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of classes on the basis of the measured features. The ANN is a non knowledge-based adaptive system, which can be effectively used to model the complex relationships between inputs and outputs or to determine patterns in data – in our case medical data. The ability to learn makes ANN networks extremely valuable to the medical industry, particularly in diagnosing illness [32] since a knowledge base of information and a training set of already diagnosed cases can be used effectively so as to learn to diagnose a disease when provided with the needed data. It was found that the network trained using the SCG algorithm was able to classify the data with a very high percentage of accuracy. The developed NN and the GUI program enables the less experienced junior doctors to arrive at a better diagnosis as it keeps the expert knowledge in an intelligent system to be used efficiently by others. One advantage of using NCS data is that feature extraction need not be done [33]. When performing analysis of complex data one of the major problems is the number of variables involved and hence dimensionality reduction techniques have to be used. But, NCS data are numerical values rather than continuous time domain signals and hence they can be used as such. But the limitations of NCS should be taken into account when interpreting the findings. There is no reliable means of studying proximal sensory nerves. NCS results can be normal in patients with small-fiber neuropathies, and lower extremity sensory responses can be absent in normal elderly patients [6]. Thus, we conclude that studies involving the use of neural network applications in providing diagnostic and predictive medical opinions are highly promising for the future. They can add value if embedded into the routine clinical consultations and used judiciously but can never completely replace the clinician. References [1] Johnsen B, Fuglsang-Frederiksen A. Electrodiagnosis of polyneuropathy. Neurophysiol Clin 2000;30(6):339–51. [2] Krarup C. An update on electrophysiological studies in neuropathy. Curr Opin Neurol 2003;16(5):603–12. [3] Atroshi I, Gummesson C, Johnsson R, Ornstein E, Ranstam J, Rosen I. Prevalence of carpal tunnel syndrome in a general population. JAMA 1999;282(2):153–8. [4] Kimura J. Principles and pitfalls of nerve conduction studies. Ann Neurol 1984;16(4):415–29. [5] Kimura J. The carpal tunnel syndrome: localization of conduction abnormalities within the distal segment of the median nerve. Brain 1979;102(3):619–35. [6] Poncelet AN. An algorithm for the evaluation of peripheral neuropathy. Am Fam Physician 1998;57(4):755–64. [7] Kiziltan E, Dalkilic N, Guney FB, Pehlivan F. Conduction velocity distribution: early diagnostic tool for peripheral neuropathies. Int J Neurosci 2007;117(2):203–13. [8] Franssen H, van den Bergh PY. Nerve conduction studies in polyneuropathy: practical physiology and patterns of abnormality. Acta Neurol Belg 2006;106(2):73–81.

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