Modeling Changes in Corneal Parameters With Age: Implications for Corneal Disease Detection.

Area of Science:

• Ophthalmology and computational modeling.
• Corneal biomechanics and age-related changes.

Background:

• Subclinical corneal ectasia (SCE) detection requires accurate normative data.
• Understanding normal age-related corneal parameter changes is crucial for identifying deviations.

Purpose of the Study:

• To model age-related corneal changes using computational methods.
• To associate these changes with demographic factors.
• To develop a framework for enhanced SCE detection.

Main Methods:

• Cross-sectional study of 117 healthy participants.
• Pentacam HR used for corneal thickness (CT), front surface sagittal curvature (FSSC), and back surface sagittal curvature (BSSC) measurements.
• Sliding window polynomial regression and cluster analyses applied to model age-related variations.

Main Results:

• Concentric clustering observed for CT and FSSC, but not BSSC.
• Quartic and cubic regression models best fit CT and FSSC/BSSC, respectively.
• Sliding window models showed improved agreement for CT and FSSC compared to decade-based models.

Conclusions:

• Developed models accurately describe age-related variations in CT and FSSC.
• Other factors influence BSSC variations.
• Findings provide a framework for SCE detection and normal database development.