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Titel: Prediction of the axial lens position after cataract surgery using deep learning algorithms and multilinear regression
VerfasserIn: Langenbucher, Achim
Szentmáry, Nóra
Cayless, Alan
Wendelstein, Jascha
Hoffmann, Peter
Sprache: Englisch
Titel: Acta Ophthalmologica
Bandnummer: 100
Heft: 7
Seiten: e1378-e1384
Verlag/Plattform: Wiley
Erscheinungsjahr: 2022
Freie Schlagwörter: anatomical lens position
axial IOL position
deep learning
optical biometry
prediction model
regression model
DDC-Sachgruppe: 610 Medizin, Gesundheit
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: Background: The prediction of anatomical axial intraocular lens position (ALP) is one of the major challenges in cataract surgery. The purpose of this study was to develop and test prediction algorithms for ALP based on deep learning strategies. Methods: We evaluated a large data set of 1345 biometric measurements from the IOLMaster 700 before and after cataract surgery. The target parameter was the intraocular lens (IOL) equator plane at half the distance between anterior and posterior apex. The relevant input parameters from preoperative biometry were extracted using a principal component analysis. A selection of neural network algorithms was tested using a 5-fold cross-validation procedure to avoid overfitting. The results were then compared with a traditional multilinear regression in terms of root mean squared prediction error (RMSE). Results: Corneal radius of curvature, axial length, anterior chamber depth, corneal thickness, lens thickness and patient age were identified as effective predictive parameters, whereas pupil size, horizontal corneal diameter and Chang–Waring chord did not enhance the model. From the tested algorithms, the Gaussian prediction regression and the Support Vector Machine algorithms performed best (RMSE = 0.2805 and 0.2731 mm), outperforming the multilinear prediction model (0.3379 mm). The mean absolute prediction error yielded 0.1998, 0.1948 and 0.2415 mm for the respective models. Conclusion: Modern prediction techniques may have the potential to outperform traditional multilinear regression techniques as they can deal easily with nonlinearities betweeninput and output parameters.However,in all cases a cross-validationis mandatory to avoid overfitting and misinterpretation of the results.
DOI der Erstveröffentlichung: 10.1111/aos.15108
URL der Erstveröffentlichung: https://onlinelibrary.wiley.com/doi/full/10.1111/aos.15108
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-376262
hdl:20.500.11880/34044
http://dx.doi.org/10.22028/D291-37626
ISSN: 1755-3768
1755-375X
Datum des Eintrags: 17-Okt-2022
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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