Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems

Abstract

The integration of artificial intelligence (AI) into musculoskeletal and ocular diagnostics has catalyzed a paradigm shift in precision medicine, enabling the detection of subclinical correlations between ocular biomarkers and systemic musculoskeletal pathologies. Convolutional neural networks (CNNs) and multimodal imaging platforms are poised to decode intricate biomechanical and vascular linkages between these systems, offering non-invasive insights into conditions such as osteoporosis, cervical spine instability, and inflammatory arthritis. Validated studies demonstrate that retinal nerve fiber layer (RNFL) thinning, choroidal thickness variations, and optic nerve microstructural changes detected via optical coherence tomography (OCT) correlate strongly with degenerative spinal conditions and joint instability. Simultaneously, AI-enhanced robotic surgical systems, trained on vast orthopedic datasets, are refining procedural accuracy through real-time intraoperative feedback derived from ocular imaging. This review synthesizes advances in AI-driven diagnostic frameworks, predictive analytics, and robotic interventions, emphasizing their role in bridging ocular and musculoskeletal health.