Postoperative intraocular lens tilt from preoperative full crystalline lens geometry using machine learning

In cataract surgery, the opacified crystalline lens is replaced by an artificial intraocular lens (IOL), requiring precise preoperative selection of parameters to optimize postoperative visual quality. Three-dimensional customized eye models, which can be constructed using quantitative data from anterior segment optical coherence tomography, provide a robust platform for virtual surgery. These models enable simulations and predictions of the optical outcomes for specific patients and selected IOL. A critical step in building these models is estimating the IOL’s tilt and position preoperatively based on the available preoperative geometrical information (ocular parameters). In this study, we present a machine learning model that, for the first time, incorporates the full shape geometry of the crystalline lens as candidate input features to predict the postoperative IOL tilt. Furthermore, we identify the most relevant features for this prediction task. Our model demonstrates significantly lower estimation errors compared to a simple linear correlation method and a state-of-the art approach that excludes full shape crystalline lens features, reducing the estimation error by approximately 5% compared to the latter. These findings highlight the potential of this approach to enhance the accuracy of postoperative predictions, paving the way to improve visual outcomes in cataract patients.