Deep learning for osteoarthritis classification in temporomandibular joint.
Oral Dis
; 29(3): 1050-1059, 2023 Apr.
Article
en En
| MEDLINE
| ID: mdl-34689379
ABSTRACT
OBJECTIVES:
This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. SUBJECTS ANDMETHODS:
A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ-OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (622). We used pretrained Resnet152 and EfficientNet-B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient-weighted class activation mapping (grad-CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists).RESULTS:
The classification accuracies of ResNet-152 and EfficientNet-B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad-CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed.CONCLUSIONS:
The artificial intelligence model improved the diagnostic power of TMJ-OA when trained with two-dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ-OA.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Osteoartritis
/
Trastornos de la Articulación Temporomandibular
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Oral Dis
Asunto de la revista:
ODONTOLOGIA
Año:
2023
Tipo del documento:
Article