{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T22:23:09Z","timestamp":1790029389106,"version":"4.0.1"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T00:00:00Z","timestamp":1614988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK\/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.<\/jats:p>","DOI":"10.3390\/jcm10051103","type":"journal-article","created":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T20:20:43Z","timestamp":1615148443000},"page":"1103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery"],"prefix":"10.3390","volume":"10","author":[{"given":"Tomofusa","family":"Yamauchi","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Tsukazaki Hospital, Himeji 671-1227, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitoshi","family":"Tabuchi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tsukazaki Hospital, Himeji 671-1227, Japan"},{"name":"Department of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima 734-8511, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kosuke","family":"Takase","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tsukazaki Hospital, Himeji 671-1227, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Masumoto","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tsukazaki Hospital, Himeji 671-1227, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1016\/j.jcrs.2005.03.065","article-title":"Refractive expectations of patients having cataract surgery","volume":"31","author":"Hawker","year":"2005","journal-title":"J. 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