1. Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal.
- Author
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Guerra Domingues, Júlio, Castro Araujo, Daniella, Costa-Silva, Luciana, Corrêa Machado, Alexei Manso, Carneiro Machado, Luciana Andrade, Alonso Veloso, Adriano, Maria Barreto, Sandhi, and Weiss Telles, Rosa
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CONVOLUTIONAL neural networks , *KNEE , *RECEIVER operating characteristic curves , *DATABASES , *RADIOGRAPHS , *MACHINE learning , *NEURAL development , *KNEE osteoarthritis - Abstract
Objective: 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. Materials and Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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