Advances in machine learning for keratoconus diagnosis.

Journal: International ophthalmology
PMID: 40159519

Abstract

PURPOSE: To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and practical implementation in clinical settings.

Authors

  • Zahra J Muhsin
    Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.
  • Rami Qahwaji
    Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK. r.s.r.qahwaji@bradford.ac.uk.
  • Ibrahim Ghafir
    Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.
  • Mo'ath AlShawabkeh
    Department of Ophthalmology, The Hashemite University, Zarqa, Jordan.
  • Muawyah Al Bdour
    Department of Ophthalmology, The University of Jordan, Amman, Jordan.
  • Saif AlRyalat
    School of Medicine, The University of Jordan, Amman, Jordan.
  • Majid Al-Taee
    , Liverpool, UK.

Keywords

External Resources

Popular Topics
Recent Journals