Multimodal diagnostics for keratoconus and ectatic corneal diseases: a paradigm shift

Area of Science:

• Ophthalmology
• Corneal Diseases
• Diagnostic Imaging

Background:

• Ectatic corneal diseases (ECD) pose diagnostic challenges, necessitating comprehensive evaluation beyond clinical history.
• Multimodal imaging and advanced diagnostics are crucial for characterizing corneal structure and biomechanics.

Purpose of the Study:

• To outline current diagnostic approaches for ectatic corneal diseases (ECD).
• To highlight the role of advanced imaging and artificial intelligence in ECD diagnosis and risk assessment.
• To discuss the paradigm shifts in ECD management, including surgical interventions and ectasia risk stratification.

Main Methods:

• Utilized Placido-disk topography, Scheimpflug tomography, corneal biomechanical evaluations, and optical coherence tomography (OCT) or ultrasound (dVHF-US) for layered imaging.
• Incorporated ocular wavefront analysis, axial length measurements, microscopy, and genetic testing.
• Leveraged artificial intelligence for data interpretation and enhanced diagnostic accuracy.

Main Results:

• Multimodal imaging provides fundamental complementary data for ECD assessment.
• Artificial intelligence significantly boosts diagnostic accuracy and facilitates clinical decisions.
• Ectasia risk assessment has evolved to identify susceptibility and progression factors, including environmental influences.

Conclusions:

• Comprehensive diagnostic strategies integrating multimodal imaging and AI are essential for ECD management.
• Individualized treatments, including crosslinking and intrastromal corneal ring segments, represent therapeutic advancements.
• Accurate ectasia risk assessment is critical for preventing iatrogenic complications after refractive surgery.