Computer aided diagnostic problem solving: Identification of peripheral nerve disorders

AimThe 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 methodsThe 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.ResultsBoth 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.DiscussionsIn 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.ConclusionsThe 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.