AI RESEARCH
Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Uncurated Hospital Data
arXiv CS.CV
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ArXi:2603.24903v1 Announce Type: new This study assesses whether self-supervised learning (SSL) improves knee osteoarthritis (OA) modeling for diagnosis and prognosis relative to ImageNet-pretrained initialization. We compared (i) image-only SSL pretrained on knee radiographs from the OAI, MOST, and NYU cohorts, and (ii) multimodal image-text SSL pretrained on uncurated hospital knee radiographs paired with radiologist impressions. For diagnostic Kellgren-Lawrence (KL) grade prediction, SSL offered mixed results.