Subclinical keratoconus detection with three-dimensional (3-D) morphogeometric and volumetric analysis

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Medical Imaging

Background:

• Subclinical keratoconus (KC) presents diagnostic challenges due to minimal visual impairment.
• Early detection of KC is crucial to prevent progressive vision loss.
• Traditional diagnostic methods may not identify subtle corneal changes in early-stage KC.

Purpose of the Study:

• To evaluate the effectiveness of three-dimensional (3-D) corneal modeling for diagnosing subclinical keratoconus (KC).
• To utilize morphogeometric and volumetric parameters derived from 3-D models for KC detection.
• To assess the potential of 3-D corneal analysis in distinguishing subclinical KC from normal corneas.

Main Methods:

• A cross-sectional study included 93 eyes with subclinical KC and 109 control eyes.
• Computer-based 3-D corneal models were generated from topographic data.
• Analysis involved distance, area, and volume parameters, including deviations of corneal apices and thinnest points, surface areas, total corneal volume, and volumetric distribution.

Main Results:

• Eyes with subclinical KC showed significantly higher anterior and posterior corneal deviations, anterior surface area, and anterior apex area compared to controls.
• Subclinical KC eyes exhibited lower posterior apex area, thinnest posterior area, total corneal volume, and volumetric parameters.
• A regression analysis formula achieved high classification accuracy: 96.8% for subclinical KC and 94.5% for normal eyes.

Conclusions:

• Subclinical KC is characterized by asymmetric displacement of corneal apices, corneal thinning, and reduced volume.
• 3-D morphogeometric and volumetric analysis offers a sensitive and specific method for detecting subclinical KC.
• Integration of these 3-D parameters into topography software can enhance clinical diagnosis of subclinical KC.