Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.

Journal: BMC ophthalmology

PMID: 39696028

Abstract

BACKGROUND: The purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.

Authors

Richul Oh

Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea .
Joo Youn Oh

Department of Ophthalmology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Hyuk Jin Choi

Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea.
Mee Kum Kim

Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea.
Chang Ho Yoon

Big Data Institute, Oxford University, Oxford, UK changho.yoon@gmail.com.

Keywords

Aged Aged, 80 and over Biometry Cataract Extraction Female Humans Lens Implantation, Intraocular Lenses, Intraocular Machine Learning Male Middle Aged Optics and Photonics Refraction, Ocular Retrospective Studies Visual Acuity

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View on PubMed Access via DOI PubMed (39696028)

Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.

Abstract

Authors

Keywords

External Resources

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