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Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts

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Artificial intelligence for detecting keratoconus.

Magali Ms Vandevenne¹, Eleonora Favuzza², Mitko Veta³

¹University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands.

The Cochrane Database of Systematic Reviews

|November 15, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) shows high accuracy in diagnosing manifest keratoconus but is less reliable for subclinical cases. Further research is needed to overcome bias and heterogeneity for clinical application.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Keratoconus is a progressive corneal disorder often diagnosed late.
Early diagnosis is challenging, relying on expert interpretation of corneal imaging.
AI offers potential for earlier detection to prevent vision loss and transplantation.

Approach:

Systematic review and meta-analysis of 63 studies (1994-2022).
Evaluated AI diagnostic accuracy for keratoconus using topography and tomography data.
Assessed AI performance for manifest and subclinical keratoconus detection.

Key Points:

AI demonstrated high accuracy for manifest keratoconus (sensitivity 98.6%, specificity 98.3%).
AI accuracy was lower for subclinical keratoconus (sensitivity 90.0%, specificity 95.5%).
Studies exhibited high risk of bias and heterogeneity, with low certainty of evidence.

Conclusions:

AI shows promise as a triage tool for keratoconus detection.
Lower accuracy for subclinical cases risks missed diagnoses and disease progression.
Reliable conclusions are limited by study bias and heterogeneity; standardization is needed.