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Titel: Prediction of total corneal power from measured anterior corneal power on the IOLMaster 700 using a feedforward shallow neural network
VerfasserIn: Langenbucher, Achim
Cayless, Alan
Szentmáry, Nóra
Weisensee, Johannes
Wendelstein, Jascha
Hoffmann, Peter
Sprache: Englisch
Titel: Acta Ophthalmologica
Bandnummer: 100 (2022)
Heft: 5
Seiten: e1080-e1087
Verlag/Plattform: Wiley
Erscheinungsjahr: 2021
Freie Schlagwörter: biometry
corneal back surface
deep learning
feedforward multi-output network
neural network
posterior corneal astigmatism
DDC-Sachgruppe: 610 Medizin, Gesundheit
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: cataract surgery with toriclensimplantation.The purpose of this studywas to set up a deeplearning algorithmwhich predicts the total corneal power from keratometry and biometric measures. Methods: Based on a large data set of measurements with the IOLMaster 700 from two clinical centres, data from N = 21 108 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°/90° (.AST0°) and 45°/135° (.AST45°) axis, a multi-output feedforward shallow neural network was derived to predict TK from K, AL, CCT, ACD, LT, W2W and patient age. Results: After some trial and error, the neural network having a Levenberg–Marquardt 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 CPpred minus TK) of the network trained with the training and cross-validated with test data showed systematically narrower distributions for CPEQTKEQ, CPAST0°-TKAST0° and CPAST45°-TKAST45° compared with KEQ-TKEQ, KAST0°-TKAST0° and KAST45°- TKAST45° . There was no systematic offset in the components between CPpred and TK. Conclusion: Unlike any fixed correction term, which can compensate only for a static intercept of the astigmatic components TKEQ,TKAST0°andTKAST45°comparedwithKEQ,KAST0°andKAST45°, our trained neural networkwas 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.
DOI der Erstveröffentlichung: 10.1111/aos.15040
URL der Erstveröffentlichung: https://onlinelibrary.wiley.com/doi/full/10.1111/aos.15040
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-411357
hdl:20.500.11880/36921
http://dx.doi.org/10.22028/D291-41135
ISSN: 1755-3768
1755-375X
Datum des Eintrags: 22-Nov-2023
Fakultät: M - Medizinische Fakultät
Fachrichtung: M - Augenheilkunde
Professur: M - Univ.-Prof. Dr. Dipl.-Ing. Achim Langenbucher
M - Prof. Dr. med. Nóra Szentmáry
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes



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