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作 者:Yosai Mori Tomofusa Yamauchi Shota Tokuda Keiichiro Minami Hitoshi Tabuchi Kazunori Miyata
机构地区:[1]Miyata Eye Hospital,6‑3 Kurahara‑cho,Miyakonojo,Miyazaki 885‑0051,Japan [2]Department of Ophthalmology,Tsukazaki Hospital,68‑1 Waku,Aboshi‑ku,Himeji,Hyogo 671‑1227,Japan [3]Department of Technology and Design Thinking for Medicine,Hiroshima University,1‑2‑3 Kasumi,Minami‑ku,Hiroshima 734‑8553,Japan
出 处:《Eye and Vision》2023年第5期32-40,共9页眼视光学杂志(英文)
摘 要:Background:To examine the effectiveness of the use of machine learning for adapting an intraocular lens(IOL)power calculation for a patient group.Methods: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 analyzed.Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients,constants of the SRK/T and Haigis formulas were optimized.The SRK/T formula was adapted using a support vector regressor.Prediction 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 patients.Mean 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 formulas.Results: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 others.The performance of the Barrett Universal II was not better than the others for the patient group.There were the least eyes with hyperopic refractive errors(16.5%)in the use of the adapted formula.Conclusions: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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