AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings
Journal:
arXiv
Published Date:
Mar 26, 2025
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, 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.
