Your browser doesn't support javascript.
loading
An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint.
Talaat, Wael M; Shetty, Shishir; Al Bayatti, Saad; Talaat, Sameh; Mourad, Louloua; Shetty, Sunaina; Kaboudan, Ahmed.
Afiliação
  • Talaat WM; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, 27272, UAE. wtaha@sharjah.ac.ae.
  • Shetty S; Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, UAE. wtaha@sharjah.ac.ae.
  • Al Bayatti S; Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Suez Canal University, Ismailia, Egypt. wtaha@sharjah.ac.ae.
  • Talaat S; Chair, Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, UAE. wtaha@sharjah.ac.ae.
  • Mourad L; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, 27272, UAE.
  • Shetty S; Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, UAE.
  • Kaboudan A; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, 27272, UAE.
Sci Rep ; 13(1): 15972, 2023 09 25.
Article em En | MEDLINE | ID: mdl-37749161
The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen's kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoartrite / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoartrite / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de publicação: Reino Unido