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August 20, 2024Cornea

Machine Learning–Based Identification of Risk Factors of Keratoconus Progression Using Raw Corneal Tomography Data

Yamit Cohen‐Tayar Hadar Cohen Dor Key Alon Tiosano Eliane Rozanes Eitan Livny Irit Bahar Yoav Nahum

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Abstract

The purpose of this study was to identify early indicators of keratoconus progression in Pentacam data using machine learning (ML) techniques.

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Cite This Study

Cohen‐Tayar et al. (2024) studied this question.

synapsesocial.com/papers/68e5b9b7b6db64358755244e https://doi.org/10.1097/ico.0000000000003669

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Predicting Keratoconus Progression From a Single Visit: Is Machine Learning Successful?Cohort Study · 2026
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4Multimodal Classification of Keratoconus Progression Using Corneal Surface Geometry and Refractive Data: The Role of Peripheral Regions and Missing-Data PatternsOriginal Research · 2026
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