Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study

• Published in: Diagnostics (Basel) Vol. 14; no. 17; p. 1900
• Main Authors: Won, Hyeyeon, Lee, Hye, Youn, Daemyung, Park, Doohyun, Eo, Taejoon, Kim, Wooju, Hwang, Dosik
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.08.2024
• Subjects: Artificial intelligence; Classification; Deep learning; Knee; knee joint effusion; orthopedic diagnosis; Orthopedics; Osteoarthritis; radiographs; Statistical analysis; visualization; X-rays; deep learning; visualization; orthopedic diagnosis; classification; radiographs; knee joint effusion
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14171900

Knee effusion, a common and important indicator of joint diseases such as osteoarthritis, is typically more discernible on magnetic resonance imaging (MRI) scans compared to radiographs. However, the use of radiographs for the early detection of knee effusion remains promising due to their cost-effectiveness and accessibility. This multi-center prospective study collected a total of 1413 radiographs from four hospitals between February 2022 to March 2023, of which 1281 were analyzed after exclusions. To automatically detect knee effusion on radiographs, we utilized a state-of-the-art (SOTA) deep learning-based classification model with a novel preprocessing technique to optimize images for diagnosing knee effusion. The diagnostic performance of the proposed method was significantly higher than that of the baseline model, achieving an area under the receiver operating characteristic curve (AUC) of 0.892, accuracy of 0.803, sensitivity of 0.820, and specificity of 0.785. Moreover, the proposed method significantly outperformed two non-orthopedic physicians. Coupled with an explainable artificial intelligence method for visualization, this approach not only improved diagnostic performance but also interpretability, highlighting areas of effusion. These results demonstrate that the proposed method enables the early and accurate classification of knee effusions on radiographs, thereby reducing healthcare costs and improving patient outcomes through timely interventions.