Ocular blood flow as a clinical observation: Value, limitations and data analysis.

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Data Science

Background:

• Ocular blood flow alterations are significant risk factors for eye diseases.
• Hemodynamic biomarkers are established independent risk factors across diverse populations.
• The interplay of blood flow with other risk factors and comorbidities remains unclear.

Purpose of the Study:

• To review current knowledge on ocular vascular physiology and pathology.
• To highlight limitations in current ocular imaging and data analysis methods.
• To propose integrating mathematical modeling and artificial intelligence for advanced risk assessment and precision medicine in ophthalmology.

Main Methods:

• Review of current literature on ocular vascular anatomy, physiology, and imaging.
• Analysis of clinical findings in glaucoma and other eye diseases.
• Exploration of mechanism-driven mathematical modeling and artificial intelligence applications.

Main Results:

• Current ocular imaging techniques provide heterogeneous, non-interchangeable biomarkers.
• Standard statistical methods yield inconsistent results due to disease complexity and imaging limitations.
• Mathematical modeling and AI offer a promising approach for integrated data analysis and individualized risk prediction.

Conclusions:

• There is a need for integrated data analysis approaches in ophthalmology.
• Mathematical modeling and AI can overcome limitations of standard statistical methods.
• These advanced methods pave the way for precision medicine in diagnosing and managing ocular diseases.