AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings
Authors:
Bargava Subramanian,
Naveen Kumarasami,
Praveen Shastry,
Raghotham Sripadraj,
Kalyan Sivasailam,
Anandakumar D,
Abinaya Ramachandran,
Sudhir MP,
Gunakutti G,
Kishore Prasath Venkatesh
Abstract:
Study Design: This study presents the development of an autonomous AI system for MRI spine pathology detection, trained on a dataset of 2 million MRI spine scans sourced from diverse healthcare facilities across India. The AI system integrates advanced architectures, including Vision Transformers, U-Net with cross-attention, MedSAM, and Cascade R-CNN, enabling comprehensive classification, segment…
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Study Design: This study presents the development of an autonomous AI system for MRI spine pathology detection, trained on a dataset of 2 million MRI spine scans sourced from diverse healthcare facilities across India. The AI system integrates advanced architectures, including Vision Transformers, U-Net with cross-attention, MedSAM, and Cascade R-CNN, enabling comprehensive classification, segmentation, and detection of 43 distinct spinal pathologies. The dataset is balanced across age groups, genders, and scanner manufacturers to ensure robustness and adaptability. Subgroup analyses were conducted to validate the model's performance across different patient demographics, imaging conditions, and equipment types.
Performance: The AI system achieved up to 97.9 percent multi-pathology detection, demonstrating consistent performance across age, gender, and manufacturer subgroups. The normal vs. abnormal classification achieved 98.0 percent accuracy, and the system was deployed across 13 major healthcare enterprises in India, encompassing diagnostic centers, large hospitals, and government facilities. During deployment, it processed approximately 100,000 plus MRI spine scans, leading to reduced reporting times and increased diagnostic efficiency by automating the identification of common spinal conditions.
Conclusion: The AI system's high precision and recall validate its capability as a reliable tool for autonomous normal/abnormal classification, pathology segmentation, and detection. Its scalability and adaptability address critical diagnostic gaps, optimize radiology workflows, and improve patient care across varied healthcare environments in India.
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Submitted 28 March, 2025; v1 submitted 26 March, 2025;
originally announced March 2025.