title: Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: a preliminary study.
abstract: OBJECTIVE
  Artificial neural networks (ANNs) have been developed in the past few decades for many different applications in medical science and in biomedical research. The use of neural networks in oral and maxillofacial surgery is limited. The aim of this study was to determine the use of ANNs for the prediction of 2 subgroups of temporomandibular joint (TMJ) internal derangements (IDs) and normal joints using characteristic clinical signs and symptoms of the diseases.
  
  
  METHODS
  Clinical symptoms and diagnoses of 161 patients with TMJ ID were considered the gold standard and were employed to train a neural network. After the training process, the symptoms and diagnoses of 58 new patients were used to verify the network's ability to diagnose. The diagnoses obtained from ANNs were compared with diagnoses of a surgeon experienced in temporomandibular disorders. The sensitivity and specificity of ANNs in predicting subtypes of TMJ ID were evaluated using clinical diagnosis as the gold standard.
  
  
  RESULTS
  Eight cases evaluated as bilaterally normal in clinical examination were evaluated as normal by ANN. In detecting unilateral anterior disc displacement with reduction (ADDwR; clicking), the sensitivity and specificity of ANN were 80% and 95%, respectively. In detecting unilateral anterior disc displacement without reduction (ADDwoR; locking), the sensitivity and specificity of ANN were 69% and 91%, respectively. In detecting bilateral ADDwoR, the sensitivity and specificity of ANN were 37% and 100%, respectively. In detecting bilateral ADDwR, the sensitivity and specificity of ANN were 100% and 89%, respectively. In detecting cases of ADDwR at 1 side and ADDwoR at the other side, the sensitivity and specificity of ANN were 44% and 93%, respectively.
  
  
  CONCLUSIONS
  The application of ANNs for diagnosis of subtypes of TMJ IDs may be a useful supportive diagnostic method, especially for dental practitioners. Further research, including advanced network models that use clinical data and radiographic images, is recommended.
authors.affiliation: Faculty of Dentistry, Department of Oral and Maxillofacial Surgery, Ondokuz Mayis University, Samsun, Turkey.
authors.forename: Burcu
authors.initials: B
authors.lastname: Bas
authors.forename: Okan
authors.initials: O
authors.lastname: Ozgonenel
authors.forename: Bora
authors.initials: B
authors.lastname: Ozden
authors.forename: Burak
authors.initials: B
authors.lastname: Bekcioglu
authors.forename: Emel
authors.initials: E
authors.lastname: Bulut
authors.forename: Murat
authors.initials: M
authors.lastname: Kurt
mesh_terms: D000465:Algorithms; D018771:Arthralgia / Q000145:classification / Q000175:diagnosis; D003657:Decision Making; D006801:Humans; D004204:Joint Dislocations / Q000145:classification / Q000175:diagnosis; D008335:Mandibular Condyle / Q000473:pathology; D016571:Neural Networks, Computer; D016059:Range of Motion, Articular / Q000502:physiology; D012680:Sensitivity and Specificity; D013016:Sound; D013701:Temporal Bone / Q000473:pathology; D019224:Temporomandibular Joint Disc / Q000473:pathology; D013705:Temporomandibular Joint Disorders / Q000145:classification / Q000175:diagnosis*