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doi:10.22028/D291-41763
Titel: | Prediction of IOL decentration, tilt and axial position using anterior segment OCT data |
VerfasserIn: | Langenbucher, Achim Szentmáry, Nóra Cayless, Alan Wendelstein, Jascha Hoffmann, Peter |
Sprache: | Englisch |
Titel: | Graefe's Archive for Clinical and experimental Ophthalmology |
Bandnummer: | 262 (2024) |
Heft: | 3 |
Verlag/Plattform: | Springer Nature |
Erscheinungsjahr: | 2023 |
Freie Schlagwörter: | IOL tilt IOL decentration Efective IOL position Geometric IOL position Anterior segment tomographer Prediction model Feedforward neural network Multilinear regression model |
DDC-Sachgruppe: | 610 Medizin, Gesundheit |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
Abstract: | Background Intraocular lenses (IOLs) require proper positioning in the eye to provide good imaging performance. This is especially important for premium IOLs. The purpose of this study was to develop prediction models for estimating IOL decentration, tilt and the axial IOL equator position (IOLEQ) based on preoperative biometric and tomographic measures. Methods Based on a dataset (N = 250) containing preoperative IOLMaster 700 and pre-/postoperative Casia2 measurements from a cataractous population, we implemented shallow feedforward neural networks and multilinear regression models to predict the IOL decentration, tilt and IOLEQ from the preoperative biometric and tomography measures. After identifying the relevant predictors using a stepwise linear regression approach and training of the models (150 training and 50 validation data points), the performance was evaluated using an N = 50 subset of test data. Results In general, all models performed well. Prediction of IOL decentration shows the lowest performance, whereas prediction of IOL tilt and especially IOLEQ showed superior performance. According to the 95% confdence intervals, decentration/ tilt/IOLEQ could be predicted within 0.3 mm/1.5°/0.3 mm. The neural network performed slightly better compared to the regression, but without signifcance for decentration and tilt. Conclusion Neural network or linear regression-based prediction models for IOL decentration, tilt and axial lens position could be used for modern IOL power calculation schemes dealing with ‘real’ IOL positions and for indications for premium lenses, for which misplacement is known to induce photic efects and image distortion. |
DOI der Erstveröffentlichung: | 10.1007/s00417-023-06208-9 |
URL der Erstveröffentlichung: | https://link.springer.com/article/10.1007/s00417-023-06208-9 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-417637 hdl:20.500.11880/37374 http://dx.doi.org/10.22028/D291-41763 |
ISSN: | 1435-702X 0721-832X |
Datum des Eintrags: | 15-Mär-2024 |
Fakultät: | M - Medizinische Fakultät |
Fachrichtung: | M - Augenheilkunde |
Professur: | 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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s00417-023-06208-9.pdf | 1,87 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons