Recurrent neural networks for diagnosis of carpal tunnel syndrome using electrophysiologic findings.
This paper presents the use of recurrent neural networks (RNNs) for diagnosis of carpal tunnel syndrome (CTS) (normal, right CTS, left CTS, bilateral CTS). The RNN is trained with the Levenberg-Marquardt algorithm. The RNN is trained on the features of CTS (right median motor latency, left median motor latency, right median sensory latency, left median sensory latency). The multilayer perceptron neural network (MLPNN) is also implemented for comparison the performance of the classifiers on the same diagnosis problem. The total classification accuracy of the RNN is significantly high (94.80%). The obtained results confirmed the validity of the RNNs to help in clinical decision-making.
Ilbay
Konuralp
K
Departmant of Neurosurgery, Kocaeli University, Kocaeli, Turkey. konuralpilbay@yahoo.com
Ubeyli
Elif Derya
ED
Ilbay
Gul
G
Budak
Faik
F
D002349:Carpal Tunnel Syndrome / Q000175:diagnosis* / Q000503:physiopathology*; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted; D004576:Electromyography; D005260:Female; D006801:Humans; D008297:Male; D009431:Neural Conduction / Q000502:physiology*; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity