Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography Using Deep Learning
Neural networks can accurately classify a patient's keratorefractive laser history from AS-OCT scans, which may support treatment planning, intraocular lens calculations, and ectasia assessment, particularly in cases where electronic health records are incomplete. This represents a step toward transforming OCT from a diagnostic to a more comprehensive screening tool in refractive clinics. <b>[<i>J Refract Surg</i>. 2025;41(3):e248-e256.]</b>.
Authors
- Jad F. Assaf (ORCID: https://orcid.org/0000-0003-4963-7339)
- Shady T. Awwad (ORCID: https://orcid.org/0000-0002-6100-0587)
- Roland Assaf
- Dan Z. Reinstein (ORCID: https://orcid.org/0000-0001-6747-7311)
- Juan Arbelaez (ORCID: https://orcid.org/0000-0001-5054-6077)
- Timothy J. Archer (ORCID: https://orcid.org/0000-0003-4196-2516)
- Diego de Ortueta (ORCID: https://orcid.org/0000-0001-9977-5460)
- María Clara Arbelaez
- Hady Yazbeck
Institutions
- American University of Beirut Medical Center (LB)
- American University of Beirut (LB)
Publication Details
- Journal
- Journal of Refractive Surgery
- Published
- 2025-03-01
- DOI
- https://doi.org/10.3928/1081597x-20250204-04
- Citations
- 4
- Primary Topic
- Corneal surgery and disorders
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Carl Zeiss Meditec AG