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Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review

Authors :
Taein Kim
Su Jeong Kim
Bo Young Lee
Hye Jin Cho
Beom Gi Sa
Ik Hee Ryu
Jin Kuk Kim
In Sik Lee
Eoksoo Han
Hyungsu Kim
Tae Keun Yoo
Source :
BMC Ophthalmology, Vol 23, Iss 1, Pp 1-11 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Background Optimal sizing for phakic intraocular lens (EVO-ICL with KS-AquaPort) implantation plays an important role in preventing postoperative complications. We aimed to formulate optimal lens sizing using ocular biometric parameters measured with a Heidelberg anterior segment optical coherence tomography (AS-OCT) device. Methods We retrospectively analyzed 892 eyes of 471 healthy subjects treated with an intraocular collamer lens (ICL) and assigned them to either the development (80%) or validation (20%) set. We built vault prediction models using the development set via classic linear regression methods as well as partial least squares and least absolute shrinkage and selection operator (LASSO) regression techniques. We evaluated prediction abilities based on the Bayesian information criterion (BIC) to select the best prediction model. The performance was measured using Pearson’s correlation coefficient and the mean squared error (MAE) between the achieved and predicted results. Results Measurements of aqueous depth (AQD), anterior chamber volume, anterior chamber angle (ACA) distance, spur-to-spur distance, crystalline lens thickness (LT), and white-to-white distance from ANTERION were highly associated with the ICL vault. The LASSO model using the AQD, ACA distance, and LT showed the best BIC results for postoperative ICL vault prediction. In the validation dataset, the LASSO model showed the strongest correlation (r = 0.582, P

Details

Language :
English
ISSN :
14712415
Volume :
23
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Ophthalmology
Publication Type :
Academic Journal
Accession number :
edsdoj.f56852161e8b44959c8989228d9553cb
Document Type :
article
Full Text :
https://doi.org/10.1186/s12886-023-02814-7