Development of machine learning-based models for vault prediction in implantable collamer lens surgery according to implant orientation.

Journal: Journal of cataract and refractive surgery

PMID: 39885651

Abstract

PURPOSE: To develop a prediction model based on machine learning to calculate the postoperative vault and the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a White population.

Authors

Timoteo González-Cruces

From the Department of Anterior Segment, Cornea and Refractive Surgery, Hospital Arruzafa, Cordoba, Spain (González-Cruces, Aguilar-Salazar, Sánchez-Ventosa, Villarrubia, Cerdán Palacios, Cano-Ortiz); Department of Anterior Segment and Refractive Surgery, Clínica Simon Oftalmología, Barcelona, Spain (Marfany Tort); Barraquer Ophthalmology Center, Barcelona, Spain (Mateu, Barraquer, Pardina); Department of Health and Biomedical Sciences, Universidad Loyola, Andalucía, Spain (Cano-Ortiz).
Francisco Javier Aguilar-Salazar
Jordi Marfany Tort
Álvaro Sánchez-Ventosa
Alberto Villarrubia
Jose Lamarca Mateu
Rafael I Barraquer
Sergio Pardina
David Cerdán Palacios
Antonio Cano-Ortiz

Keywords

Adult Anterior Eye Segment Biometry Datasets as Topic Female Humans Lens Implantation, Intraocular Machine Learning Male Middle Aged Myopia Phakic Intraocular Lenses Random Allocation Retrospective Studies Tomography, Optical Coherence Visual Acuity Young Adult

External Resources

View on PubMed Access via DOI PubMed (39885651)

Development of machine learning-based models for vault prediction in implantable collamer lens surgery according to implant orientation.

Abstract

Authors

Keywords

External Resources

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