{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:08:51Z","timestamp":1771067331401,"version":"3.50.1"},"reference-count":25,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"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":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The corneal back surface is known to add some astigmatism against\u2010the\u2010rule, which has to be considered in cataract surgery with toric lens implantation. The purpose of this study was to set up a deep learning algorithm which predicts the total corneal power from keratometry and biometric measures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Based on a large data set of measurements with the IOLMaster 700 from two clinical centres, data from <jats:italic>N<\/jats:italic>\u2009=\u200921\u2009108 eyes were included, each record containing valid data for keratometry K, total keratometry TK, axial length AL, central corneal thickness CCT, anterior chamber depth ACD, lens thickness LT and horizontal corneal diameter W2W from an individual eye. After a vector decomposition of K and TK into equivalent power (.EQ) and projections of astigmatism to the 0\u00b0\/90\u00b0 (.AST<jats:sub>0\u00b0<\/jats:sub>) and 45\u00b0\/135\u00b0 (.AST<jats:sub>45\u00b0<\/jats:sub>) axis, a multi\u2010output feedforward shallow neural network was derived to predict TK from K, AL, CCT, ACD, LT, W2W and patient age.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>After some trial and error, the neural network having a Levenberg\u2013Marquardt training function and three hidden layers (10\/8\/5 neurons) performed best and showed a fast convergence. The data set was split into training data (70%), validation data (15%) and test data (15%). The prediction error (predicted corneal power CP<jats:sub>pred<\/jats:sub> minus TK) of the network trained with the training and cross\u2010validated with test data showed systematically narrower distributions for CPEQ\u2010TKEQ, CPAST<jats:sub>0\u00b0<\/jats:sub>\u2010TKAST<jats:sub>0\u00b0<\/jats:sub> and CPAST<jats:sub>45\u00b0<\/jats:sub>\u2010TKAST<jats:sub>45\u00b0<\/jats:sub> compared with KEQ\u2010TKEQ, KAST<jats:sub>0\u00b0<\/jats:sub>\u2010TKAST<jats:sub>0\u00b0<\/jats:sub> and KAST<jats:sub>45\u00b0<\/jats:sub>\u2010TKAST<jats:sub>45\u00b0<\/jats:sub> . There was no systematic offset in the components between CP<jats:sub>pred<\/jats:sub> and TK.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Unlike any fixed correction term, which can compensate only for a static intercept of the astigmatic components TKEQ, TKAST<jats:sub>0\u00b0<\/jats:sub> and TKAST<jats:sub>45\u00b0<\/jats:sub> compared with KEQ, KAST<jats:sub>0\u00b0<\/jats:sub> and KAST<jats:sub>45\u00b0<\/jats:sub>, our trained neural network was able to reduce the variance in the prediction error significantly. This neural network could be used to account for the corneal back surface astigmatism for biometers where the corneal back surface measurement or total keratometry is not available.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aos.15040","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T20:28:24Z","timestamp":1634761704000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Prediction of total corneal power from measured anterior corneal power on the IOLMaster 700 using a feedforward shallow neural network"],"prefix":"10.1111","volume":"100","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":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Cayless","sequence":"additional","affiliation":[{"name":"School of Physical Sciences The Open University  Milton Keynes UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N\u00f3ra","family":"Szentm\u00e1ry","sequence":"additional","affiliation":[{"name":"Dr. Rolf M. 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