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

Journal: Cornea
PMID: 39167613

Abstract

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

Authors

  • Yamit Cohen-Tayar
    Department of Ophthalmology, Rabin Medical Center - Beilinson Hospital, Petach Tikva, Israel.
  • Hadar Cohen
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel .
  • Dor Key
    Department of Ophthalmology, Rabin Medical Center - Beilinson Hospital, Petach Tikva, Israel.
  • Alon Tiosano
    Department of Ophthalmology, Rabin Medical Center (Beilinson Campus), Petah Tikva, Israel, Ophthalmology Artificial Intelligence Center, Rabin Medical Center (Beilinson Campus), Petah Tikva, Israel, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Eliane Rozanes
    Department of Ophthalmology, Rabin Medical Center - Beilinson Hospital, Petach Tikva, Israel.
  • Eitan Livny
    Department of Ophthalmology, Rabin Medical Center - Beilinson Hospital, Petach Tikva, Israel.
  • Irit Bahar
    Department of Ophthalmology, Rabin Medical Center (Beilinson Campus), Petah Tikva, Israel, Ophthalmology Artificial Intelligence Center, Rabin Medical Center (Beilinson Campus), Petah Tikva, Israel, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Yoav Nahum
    Department of Ophthalmology, Rabin Medical Center - Beilinson Hospital, Petach Tikva, Israel.

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