A Multi-Site Study on AI-Driven Pathology Detection and Osteoarthritis Grading from Knee X-Ray

• URL: http://arxiv.org/abs/2503.22176v1
• Date: Fri, 28 Mar 2025 06:41:22 GMT
• Title: A Multi-Site Study on AI-Driven Pathology Detection and Osteoarthritis Grading from Knee X-Ray
• Authors: Bargava Subramanian, Naveen Kumarasami, Praveen Shastry, Kalyan Sivasailam, Anandakumar D, Keerthana R, Mounigasri M, Abilaasha G, Kishore Prasath Venkatesh,
• Abstract summary: Bone health disorders like osteoarthritis and osteoporosis pose major global health challenges.<n>This study presents an AI-powered system that analyzes knee X-rays to detect key pathologies.<n>It also grades osteoarthritis severity, enabling timely, personalized treatment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Introduction: Bone health disorders like osteoarthritis and osteoporosis pose major global health challenges, often leading to delayed diagnoses due to limited diagnostic tools. This study presents an AI-powered system that analyzes knee X-rays to detect key pathologies, including joint space narrowing, sclerosis, osteophytes, tibial spikes, alignment issues, and soft tissue anomalies. It also grades osteoarthritis severity, enabling timely, personalized treatment. Study Design: The research used 1.3 million knee X-rays from a multi-site Indian clinical trial across government, private, and SME hospitals. The dataset ensured diversity in demographics, imaging equipment, and clinical settings. Rigorous annotation and preprocessing yielded high-quality training datasets for pathology-specific models like ResNet15 for joint space narrowing and DenseNet for osteoarthritis grading. Performance: The AI system achieved strong diagnostic accuracy across diverse imaging environments. Pathology-specific models excelled in precision, recall, and NPV, validated using Mean Squared Error (MSE), Intersection over Union (IoU), and Dice coefficient. Subgroup analyses across age, gender, and manufacturer variations confirmed generalizability for real-world applications. Conclusion: This scalable, cost-effective solution for bone health diagnostics demonstrated robust performance in a multi-site trial. It holds promise for widespread adoption, especially in resource-limited healthcare settings, transforming bone health management and enabling proactive patient care.

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