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doi:10.22028/D291-37626
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 |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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Acta Ophthalmologica - 2022 - Langenbucher - Prediction of the axial lens position after cataract surgery using deep.pdf | 907,24 kB | Adobe PDF | Öffnen/Anzeigen |
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