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

Abstract

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 (p < 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.

Keywords

• artificial intelligence
• classification
• convolutional neural network
• deep learning
• knee radiographs
• subchondral sclerosis