Please use this identifier to cite or link to this item: doi:10.22028/D291-41135
Title: Prediction of total corneal power from measured anterior corneal power on the IOLMaster 700 using a feedforward shallow neural network
Author(s): Langenbucher, Achim
Cayless, Alan
Szentmáry, Nóra
Weisensee, Johannes
Wendelstein, Jascha
Hoffmann, Peter
Language: English
Title: Acta Ophthalmologica
Volume: 100 (2022)
Issue: 5
Pages: e1080-e1087
Publisher/Platform: Wiley
Year of Publication: 2021
Free key words: biometry
corneal back surface
deep learning
feedforward multi-output network
neural network
posterior corneal astigmatism
DDC notations: 610 Medicine and health
Publikation type: Journal Article
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 of the first publication: 10.1111/aos.15040
URL of the first publication: https://onlinelibrary.wiley.com/doi/full/10.1111/aos.15040
Link to this record: 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
Date of registration: 22-Nov-2023
Faculty: M - Medizinische Fakultät
Department: M - Augenheilkunde
Professorship: M - Univ.-Prof. Dr. Dipl.-Ing. Achim Langenbucher
M - Prof. Dr. med. Nóra Szentmáry
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes



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