{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:18:30Z","timestamp":1787015910656,"version":"build-2736575974"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"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":["Ophthalmic Physiologic Optic"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. This study aimed to set up and validate a deep learning algorithm to predict corneal back surface power from the corneal front surface power and biometric measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study was based on a large dataset of IOLMaster 700\u00a0measurements from two clinical centres.\n                      <jats:italic>N<\/jats:italic>\n                      \u00a0=\u00a019,553\u00a0measurements of 19,553 eyes with valid corneal front (CFSPM) and back surface power (CBSPM) data and other biometric measures. After a vector decomposition of CFSPM and CBSPM into equivalent power and projections of astigmatism to the 0\u00b0\/90\u00b0 and 45\u00b0\/135\u00b0 axes, a multi\u2010output feedforward neural network was derived to predict vector components of CBSPM from CFSPM and other measurements. The predictions were compared with a multivariate linear regression model based on CFSPM components only.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>After pre\u2010conditioning, a network with two hidden layers each having 12 neurons was derived. The dataset was split into training (70%), validation (15%) and test (15%) subsets. The prediction error (predicted corneal back surface power CBSPP \u2013 CBSPM) of the network after training and crossvalidation showed no systematic offset, narrower distributions for CBSPP \u2013 CBSPM and no trend error of CBSPP \u2013 CBSPM vs. CBSPM for any of the vector components. The multivariate linear model also showed no systematic offset, but broader distributions of the prediction error components and a systematic trend of all vector components vs. CFSPM components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The neural network approach based on CFSPM vector components and other biometric measures outperforms the multivariate linear model in predicting corneal back surface power vector components. Modern biometers can supply all parameters required for this algorithm, enabling reliable predictions for corneal back surface data where direct corneal back surface data are unavailable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/opo.12909","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T07:12:16Z","timestamp":1635837136000},"page":"185-194","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Prediction of corneal back surface power \u2013 Deep learning algorithm versus multivariate regression"],"prefix":"10.1007","volume":"42","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8019-1481","authenticated-orcid":false,"given":"N\u00f3ra","family":"Szentm\u00e1ry","sequence":"additional","affiliation":[{"name":"Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research Saarland University Homburg\/Saar Germany"},{"name":"Department of Ophthalmology Semmelweis\u2010University Budapest Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"Cayless","sequence":"additional","affiliation":[{"name":"School of Physical Sciences The Open University Milton Keynes UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Weisensee","sequence":"additional","affiliation":[{"name":"Department of Experimental Ophthalmology Saarland University Homburg\/Saar Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4145-2559","authenticated-orcid":false,"given":"Jascha","family":"Wendelstein","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Johannes Kepler University Linz Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Hoffmann","sequence":"additional","affiliation":[{"name":"Augen\u2010 und Laserklinik Castrop\u2010Rauxel Castrop\u2010Rauxel 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