CLIP-KOA: Enhancing Knee Osteoarthritis Diagnosis with Multi-Modal Learning and Symmetry-Aware Loss Functions

• URL: http://arxiv.org/abs/2504.19443v1
• Date: Mon, 28 Apr 2025 03:10:24 GMT
• Title: CLIP-KOA: Enhancing Knee Osteoarthritis Diagnosis with Multi-Modal Learning and Symmetry-Aware Loss Functions
• Authors: Yejin Jeong, Donghun Lee,
• Abstract summary: We propose a CLIP-based framework (CLIP-KOA) to enhance the consistency and reliability of KOA grade prediction.<n>CLIP-KOA achieves state-of-the-art accuracy of 71.86% on KOA severity prediction task.
• Score: 2.9854771359202275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knee osteoarthritis (KOA) is a universal chronic musculoskeletal disorders worldwide, making early diagnosis crucial. Currently, the Kellgren and Lawrence (KL) grading system is widely used to assess KOA severity. However, its high inter-observer variability and subjectivity hinder diagnostic consistency. To address these limitations, automated diagnostic techniques using deep learning have been actively explored in recent years. In this study, we propose a CLIP-based framework (CLIP-KOA) to enhance the consistency and reliability of KOA grade prediction. To achieve this, we introduce a learning approach that integrates image and text information and incorporate Symmetry Loss and Consistency Loss to ensure prediction consistency between the original and flipped images. CLIP-KOA achieves state-of-the-art accuracy of 71.86\% on KOA severity prediction task, and ablation studies show that CLIP-KOA has 2.36\% improvement in accuracy over the standard CLIP model due to our contribution. This study shows a novel direction for data-driven medical prediction not only to improve reliability of fine-grained diagnosis and but also to explore multimodal methods for medical image analysis. Our code is available at https://github.com/anonymized-link.

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