Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected...

Area of Science:

• Ophthalmology and Musculoskeletal Health
• Biomedical Engineering
• Precision Medicine

Background:

• Precision medicine increasingly utilizes AI for diagnostics.
• Ocular biomarkers are emerging indicators of systemic health.
• Musculoskeletal disorders pose significant health challenges.

Purpose of the Study:

• To review evidence linking AI-analyzed ocular parameters to musculoskeletal conditions.
• To explore the eye as a non-invasive diagnostic window for biomechanical dysfunction.
• To discuss AI's role in developing new diagnostic and surgical tools for musculoskeletal diseases.

Main Methods:

• Systematic review of studies integrating AI with ocular and musculoskeletal imaging.
• Analysis of optical coherence tomography (OCT)-derived parameters (e.g., RNFL thickness, choroidal thickness).
• Correlation of ocular microstructural changes with systemic degenerative pathologies.

Main Results:

• Consistent correlations found between ocular microstructural changes and systemic degenerative musculoskeletal conditions.
• AI enhances the identification of diagnostic links between eye and musculoskeletal system.
• Ocular metrics show potential for non-invasive assessment of biomechanical dysfunction.

Conclusions:

• AI-driven ocular analysis is a promising tool for early detection of musculoskeletal diseases.
• The eye can serve as a non-invasive window for diagnosing systemic biomechanical issues.
• Interdisciplinary approaches combining AI, ophthalmology, and rheumatology hold significant clinical potential.