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

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)44-52
Number of pages9
JournalStereotactic and Functional Neurosurgery
Volume92
Issue number1
DOIs
StatePublished - Jan 2014

Fingerprint

Facial Neuralgia
Facial Pain
Trigeminal Neuralgia
Neuralgia
Glossopharyngeal Nerve Diseases
Temporomandibular Joint Disorders
Postherpetic Neuralgia
Sensitivity and Specificity
Aptitude
Surveys and Questionnaires

Keywords

  • Artificial intelligence
  • Facial pain
  • Neural networks
  • Trigeminal neuralgia

ASJC Scopus subject areas

  • Clinical Neurology
  • Surgery

Cite this

Use of an artificial neural network for diagnosis of facial pain syndromes : An update. / McCartney, Shirley; Weltin, Markus; Burchiel, Kim.

In: Stereotactic and Functional Neurosurgery, Vol. 92, No. 1, 01.2014, p. 44-52.

Research output: Contribution to journalArticle

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