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Purpose: To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoperative biometry data. Setting: Kepler University Clinic Linz, Linz, Austria. Design: Prospective single-center study. Methods: Optical coherence tomography, autorefraction, and subjective refraction were performed at baseline and 8 weeks after cataract surgery. In analysis I, only 1 eye per patient was included and a tilt prediction model was generated. In analysis II, a pairwise comparison between right and left eyes was performed. Results: In analysis I, 50 eyes of 50 patients were analyzed. Difference in amount, orientation, and vector from preoperative to postoperative lens tilt was -0.13 degrees, 2.14 degrees, and 1.20 degrees, respectively. A high predictive power (variable importance for projection [VIP]) for postoperative tilt prediction was found for preoperative tilt (VIP = 2.2), pupil decentration (VIP = 1.5), lens thickness (VIP = 1.1), axial eye length (VIP = 0.9), and preoperative lens decentration (VIP = 0.8). These variables were applied to a machine learning algorithm resulting in an out of bag score of 0.92 degrees. In analysis II, 76 eyes of 38 patients were included. The difference of preoperative to postoperative IOL tilt of right and left eyes of the same individual was statistically relevant. Conclusions: Postoperative IOL tilt showed excellent predictability using preoperative biometry data and a combination of partial least squares regression and a machine learning algorithm. Preoperative lens tilt, pupil decentration, lens thickness, axial eye length, and preoperative lens decentration were found to be the most relevant parameters for this prediction model. [ABSTRACT FROM AUTHOR]

Purpose: To comprehensively evaluate the tilt of the crystalline lens in phakic eyes and its relationship with optical biometry parameters., Setting: The retrospective analysis was conducted at the Department for Ophthalmology and Optometry, Kepler University Clinic in Linz, Austria., Design: Lens tilt assessment was performed through a combined analysis of optical biometry (IOL Master 700) and anterior segment optical coherence tomography (AS-OCT) data to understand its relationship., Methods: The study analyzed preoperative biometry data and AS-OCT images from 4689 eyes of 2489 patients prior to cataract surgery. The optical biometry parameters were correlated with the degree of lens tilt using logistic regression and machine learning techniques., Results: The average lens tilt was 4.9°, with 6.7% of eyes showing a tilt greater than 7°. Significant associations were found with specific biometry parameters, such as anterior chamber depth (ACD), axial length (AL), equator thickness and lens decentration. The developed predictive model for assessing tilt risk demonstrated a high accuracy with an area under the curve of 0.862., Conclusions: The findings highlight the average lens tilt of 4.9° in a cataract patient cohort. This analysis contributes to a broader understanding of lens orientation and its interactions with biometric parameters., (Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of ASCRS and ESCRS.)