[KERATOCONUS: DIAGNOSIS AND INNOVATIONS]

Area of Science:

• Ophthalmology
• Corneal Diseases
• Diagnostic Technologies

Background:

• Keratoconus was historically underdiagnosed due to limited imaging technology, leading to late detection and severe complications like corneal transplants.
• Early detection is vital for managing keratoconus, preventing progression, and planning family screening due to its genetic component.

Purpose of the Study:

• To review the latest advancements in keratoconus diagnostic tools and strategies.
• To highlight novel approaches for early detection and intervention in keratoconus.

Main Methods:

• Review of current literature on keratoconus diagnosis.
• Focus on advancements in molecular diagnostics, corneal imaging, and artificial intelligence applications.
• Analysis of techniques including corneal topography, epithelial mapping, Bowman layer imaging, aberration analysis, and biomechanics.

Main Results:

• Modern corneal imaging offers detailed visualization of corneal layers, aiding in detecting subtle ectatic changes.
• Corneal biomechanics evaluation and analysis of anterior corneal aberrations enhance diagnostic accuracy.
• Artificial intelligence integrated with imaging devices represents a significant frontier in early keratoconus detection.

Conclusions:

• Novel diagnostic tools, particularly advanced imaging and AI, enable earlier and more accurate detection of keratoconus.
• Early diagnosis facilitates timely interventions like corneal cross-linking, slowing disease progression.
• Improved screening strategies are essential for individuals at risk and for preventing post-refractive surgery ectasia.