International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 6
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Early Osteoarthritis Detection from Knee X-Rays

Author(s) Prof. Harshith V, Mohan Gowda L, Namish Jain A, Swapnil Divate
Country India
Abstract Millions of people worldwide suffer from osteoarthritis (OA), a degenerative joint disease for which early identification is essential to slowing progression and improving patient outcomes. Even though radiographic imaging—especially X-rays—remains the most affordable and widely available diagnostic method, doctors sometimes struggle to spot minor alterations in the early stages (Kellgren–Lawrence grades 0–1). Recent advances in deep learning and artificial intelligence (AI) create new opportunities for automated early OA identification and grading on radiographs. This paper examines cutting-edge methods, stressing both their advantages and disadvantages. We propose a methodological overview for developing an automated system that integrates preprocessing, region-of-interest localization, and classification to improve sensitivity in early OA detection. There includes discussion of difficulties such poor generalizability across datasets, class imbalance, and interpretability problems. Finally, we outline future research initiatives, stressing the necessity of integrating radiomic biomarkers, developing clinically interpretable AI models, and obtaining larger multi-center datasets.
Keywords Osteoarthritis, Knee radiograph, Early identification, Kellgren-Lawrence grades, Deep learning, Convolutional neural networks (CNN), Transfer learning, Radiomics, Medical image analysis, Explainable AI, Radiographic images, Machine learning, ROI, classification, and preprocessing.
Field Medical / Pharmacy
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-24
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61534
Short DOI https://doi.org/hbcbbc
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E-ISSN 2582-2160
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