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Machine learning adaptation of intraocular lens power calculation for a patient group

Eye and Vision 2021年第5期8卷 422-430页

作者： yosai mori Tomofusa Yamauchi Shota Tokuda Keiichiro Minami Hitoshi Tabuchi Kazunori Miyata Miyata Eye Hospital 6‑3 Kurahara‑choMiyakonojoMiyazaki 885‑0051Japan Department of Ophthalmology Tsukazaki Hospital68‑1 WakuAboshi‑kuHimejiHyogo 671‑1227Japan Department of Technology and Design Thinking for Medicine Hiroshima University1‑2‑3 KasumiMinami‑kuHiroshima 734‑8553Japan

Background:To examine the effectiveness of the use of machine learning for adapting an intraocular lens(IOL)power calculation for a patient ***:In this retrospective study,the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL(SN60WF,Alcon)at Miyata Eye Hospital were reviewed and *** biometric metrics and postoperative refractions of 1211 eyes of 769 patients,constants of the SRK/T and Haigis formulas were *** SRK/T formula was adapted using a support vector *** errors in the use of adapted formulas as well as the SRK/T,Haigis,Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct *** prediction errors,median absolute errors,and percentages of eyes within±0.25 D,±0.50 D,and±1.00 D,and over+0.50 D of errors were compared among ***:The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas(P<0.001).In the absolute errors,the Hill-RBF and adapted methods were better than *** performance of the Barrett Universal II was not better than the others for the patient *** were the least eyes with hyperopic refractive errors(16.5%)in the use of the adapted ***:Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

关键词： Machine learning Adaptation Intraocular lens power calculation Patient ethnicity Patient race Region of patient SRK/T formula

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