Design of an artificial neural network for diagnosis of facial pain syndromes.
A classification scheme for facial pain syndromes describing seven categories has previously been proposed. Based on this classification scheme and a binomial (yes/no) facial pain questionnaire, we have designed and trained an artificial neural network (ANN) and as an initial feasibility assessment of such an ANN system examined its ability to recognize and correctly diagnose patients with different facial pain syndromes. One hundred patients with facial pain were asked to respond to a facial pain questionnaire at the time of their initial visit. After interview, an independent diagnosis was assigned to each patient. The patients' responses to the questionnaire and their diagnoses were input to an ANN. The ANN was able to retrospectively predict the correct diagnosis for 95 of 100 patients (95%), and prospectively determine a correct diagnosis of trigeminal neuralgia Type 1 with 84% sensitivity and 83% specificity in 43 new patients. The ability of the ANN to accurately predict a correct diagnosis for the remaining types of facial pain was limited by our clinic sample size and hence less exposure to those categories. This is the first demonstration of the utilization of an ANN to diagnose facial pain syndromes.
Limonadi
Farhad M
FM
Department of Neurological Surgery, Oregon Health & Science University, Portland, OR 97239-3098, USA.
McCartney
Shirley
S
Burchiel
Kim J
KJ
D000328:Adult; D000368:Aged; D005156:Facial Neuralgia / Q000145:classification / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D014277:Trigeminal Neuralgia / Q000145:classification / Q000175:diagnosis