{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:53:21Z","timestamp":1785164001112,"version":"3.55.0"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Ophthalmologica"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The purpose of this study is to compare the reconstructed corneal power (RCP) by working backwards from the post\u2010implantation spectacle refraction and toric intraocular lens power and to develop the models for mapping preoperative keratometry and total corneal power to RCP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Retrospective single\u2010centre study involving 442 eyes treated with a monofocal and trifocal toric IOL (Zeiss TORBI and LISA). Keratometry and total corneal power were measured preoperatively and postoperatively using IOLMaster 700. Feedforward neural network and multilinear regression models were derived to map keratometry and total corneal power vector components (equivalent power EQ and astigmatism components C0 and C45) to the respective RCP components.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Mean preoperative\/postoperative C0 for keratometry and total corneal power was \u22120.14\/\u22120.08 dioptres and \u22120.30\/\u22120.24 dioptres. All mean C45 components ranged between \u22120.11 and \u22120.20 dioptres. With crossvalidation, the neural network and regression models showed comparable results on the test data with a mean squared prediction error of 0.20\/0.18 and 0.22\/0.22 dioptres<jats:sup>2<\/jats:sup> and on the training data the neural network models outperformed the regression models with 0.11\/0.12 and 0.22\/0.22 dioptres<jats:sup>2<\/jats:sup> for predicting RCP from preoperative keratometry\/total corneal power.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Based on our dataset, both the feedforward neural network and multilinear regression models showed good precision in predicting the power vector components of RCP from preoperative keratometry or total corneal power. With a similar performance in crossvalidation and a simple implementation in consumer software, we recommend implementation of regression models in clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aos.16742","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T10:40:46Z","timestamp":1721126446000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Homburg\u2010Adelaide toric IOL nomogram: How to predict corneal power vectors from preoperative IOLMaster 700 keratometry and total corneal power in toric IOL implantation"],"prefix":"10.1111","volume":"103","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-6177","authenticated-orcid":false,"given":"Achim","family":"Langenbucher","sequence":"first","affiliation":[{"name":"Department of Experimental Ophthalmology Saarland University  Homburg\/Saar Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N\u00f3ra","family":"Szentm\u00e1ry","sequence":"additional","affiliation":[{"name":"Dr. Rolf M. 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