Early detection and classification of bone marrow changes in lumbar vertebrae using machine learning techniques

Abstract

Bone marrow changes in lumbar vertebrae (BMCLVB) have emerged as a significant correlation of chronic low back pain (CLBP) severity, especially in patients with comorbid conditions like HIV, osteoporosis, and cancer. Identifying these correlations not only aids governments and health insurance providers but also facilitates early treatment for those at risk. However, challenges lurk due to the unavailability and quality of healthcare data. The collaboration between data science and artificial intelligence, particularly machine learning (ML), has propelled biomedical research forward. So far, accessing and processing hospital and clinical data remains a hurdle. In doing so it aims to provide an opportunity for early intervention and treatment. In addition, the goal of the current study was to overcome data shortcomings using advanced ML techniques to unlock complex magnetic resonance imaging (MRI) features. We believe that extending the dataset with that obtained from an Iraqi hospital will not only assist in diagnosing BMCLVB but also fill the gap between data science and healthcare. Above all, the upgrade is intended to empower biomedical research and increase the chances of successful patient treatment.