ARTIFICIAL NEURAL NETWORKS IN THE IDENTIFICATION OF PERIPHERAL NERVE DISORDERS

Abstract

The recent years have witnessed an increase in the use of newer analytical tools in the field of medicine to assist in diagnostic procedure. Among the new tools, artificial neural networks (ANNs) have received particular attention because of their ability to analyze complex nonlinear data sets. This study suggests that ANNs can be used for the diagnosis of peripheral nerve disorders particularly the carpal tunnel syndrome (CTS) and neuropathy. This paper aims at building a classifier using a feed forward neural network that can distinguish between CTS, neuropathy, and normal controls using a reduced set of measurements or features from nerve conduction study (NCS) data. Three different ANN training algorithms, viz. Levenberg–Marquardt (LM), Conjugate gradient (CGB), and resilient back-propagation (RP) are used to see which algorithm produces better results and has faster training for the application under consideration. The data used were obtained from the Neurology Department, Kannur Medical College, Kerala, India. The obtained resultant confusion matrix indicated only a few misclassifications in all the three cases. The analysis showed that the CGB and RB algorithms provide faster convergence on pattern recognition problems, but the best performance in terms of accuracy is given by the LM algorithm. The accuracy obtained for the LM, CGB, and RB were 98.3%, 97.8%, and 97.2%, respectively. The respective sensitivities were 96.1%, 94.1%, and 94.1%, while the specificities were found to be equal to 99.4%, 98.8%, and 97.5%, respectively. The study aims at showing that ANNs may prove useful in combination with other systems in providing diagnostic and predictive medical opinions. However, it must always be kept in mind that ANNs represent only one form of computer-aided diagnosis, and the clinician's responsibility and overall control of patient care should never be underestimated.