Use of an artificial neural network for diagnosis of facial pain syndromes: an update.

BACKGROUND

Based on a classification scheme for facial pain syndromes and a binomial (yes/no) facial pain questionnaire, we previously reported on the ability of an artificial neural network (ANN) to recognize and correctly diagnose patients with different facial pain syndromes.

OBJECTIVES

We now report on an updated questionnaire, the development of a secure web-based neural network application and details of ANNs trained to diagnose patients with different facial pain syndromes.

METHODS

Online facial pain questionnaire responses collected from 607 facial pain patients (395 female, 65%, ratio F/M 1.86/1) over 5 years and 7 months were used for ANN training.

RESULTS

Sensitivity and specificity of the currently running ANN for trigeminal neuralgia type 1 and trigeminal neuralgia type 2 are 92.4 and 62.5% and 87.8 and 96.4%, respectively. Sensitivity and specificity are 86.7 and 95.2% for trigeminal neuropathic pain, 0 and 100% for trigeminal deafferentation pain and 100% for symptomatic trigeminal neuralgia and postherpetic neuralgia. Sensitivity is 50% for nervus intermedius neuralgia (NIN) and 0% for atypical facial pain (AFP), glossopharyngeal neuralgia (GPN) and temporomandibular joint disorder (TMJ). Specificity for AFP, NIN and TMJ is 99% and for GPN, 100%.

CONCLUSIONS

We demonstrate the utilization of question-based historical self-assessment responses used as inputs to design an ANN for the purpose of diagnosing facial pain syndromes (outputs) with high accuracy.