Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence

Osteoarthritis (OA) is the most common joint disease, affecting over 300 million people worldwide. Subchondral sclerosis is a key indicator of OA. Currently, the diagnosis of subchondral sclerosis is primarily based on radiographic images; however, reliability issues exist owing to subjective evaluations and inter-observer variability. This study proposes a novel diagnostic method that utilizes artificial intelligence (AI) to automatically classify the severity of subchondral sclerosis. A total of 4019 radiographic images of the knee were used to train the 3-Layer CNN, DenseNet121, MobileNetV2, and EfficientNetB0 models. The best-performing model was determined based on sensitivity, specificity, accuracy, and area under the curve (AUC). The proposed model exhibited outstanding performance, achieving 84.27 ± 1.03% sensitivity, 92.46 ± 0.49% specificity, 84.70 ± 0.98% accuracy, and 95.17 ± 0.41% AUC. The analysis of variance confirmed significant performance differences across models, age groups, and sexes (<i>p</i> < 0.05). These findings demonstrate the utility of AI in diagnosing and treating knee subchondral sclerosis and suggest that this approach could provide a new diagnostic method in clinical medicine. By precisely classifying the grades of subchondral sclerosis, this method contributes to improved overall diagnostic accuracy and offers valuable insights for clinical decision-making.