Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal

• Published in: Radiologia brasileira 2023-09, Vol.56 (5), p.248-254
• Main Authors: Domingues, Júlio Guerra, Araujo, Daniella Castro, Costa-Silva, Luciana, Machado, Alexei Manso Corrêa, Machado, Luciana Andrade Carneiro, Veloso, Adriano Alonso, Barreto, Sandhi Maria, Telles, Rosa Weiss
• Format: Article
• Language: English
• Subjects: Original; RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
• ISSN: 0100-3984; 1678-7099; 1678-7099
• DOI: 10.1590/0100-3984.2023.0020-en

To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.