Neural networks--an artificial intelligence approach to the analysis of clinical data

This paper describes an approach to the pattern recognition problem of categorizing evoked response waveforms, using that branch of artificial intelligence known as neural networking. A total of 19 subjects (38 eyes) were exposed to a reversing chequerboard pattern, and the resulting Pattern electroretinogram (PERG) recorded. This was repeated for each of the subjects at contrast levels of 75%,50% and 25%, with the resulting response becoming less pronounced as the contrast level was decreased. The subjects were also given an Arden contrast sensitivity test, and the resulting scores recorded for each eye. The averaged responses from each of the 3 contrast levels for each of the 38 eyes tested were used as the input to a computer simulated 3-input per neuron cascaded neural network. The output and weighting parameters for the individual neurons were then varied until the network was able to optimally classify the PERG results on the basis of the Arden contrast sensitivity scores. The neural network was thus taught to recognize patterns in the PERG responses in much the same way that an experienced clinician might. A second neural network was similarly taught to classify the PERG responses on the basis of a clinician's subjective rating of each of the responses.