[Detection of Subclinical Keratoconus]

Area of Science:

• Ophthalmology
• Corneal Science
• Refractive Surgery

Background:

• Subclinical keratoconus (KC) presents without classic clinical signs, posing a risk for refractive surgery.
• Early detection of KC is vital to prevent post-operative complications.

Purpose of the Study:

• To review current methods for identifying risk factors and detecting subclinical keratoconus.
• To provide an overview of KC epidemiology, etiology, and pathophysiology.

Main Methods:

• Review of ocular aberrometry, corneal topography, Zernike and Fourier analysis.
• Analysis of anterior segment tomography, posterior corneal surface, pachymetry, and biomechanical properties.
• Inclusion of traditional methods like retinoscopy and ophthalmoscopy.

Main Results:

• Subclinical KC detection requires a multi-modal approach beyond standard indices.
• Tomography combined with biomechanics and artificial intelligence shows promise for early detection.
• Traditional methods can offer hints but are insufficient alone.

Conclusions:

• Comprehensive pre-refractive surgery analysis, including anamnesis and advanced diagnostics, is essential.
• Integrating corneal biomechanics into diagnosis is ideal for identifying early-stage KC.
• Future trends point towards AI-driven analysis of tomography and biomechanics for subclinical KC detection.