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

Introduction to the Feature Issue "Improving Vision through Intraocular Lenses": a tribute to Jim Schwiegerling

Cataract surgery, a transformative procedure to restore vision, has seen remarkable advancements in intraocular lens (IOL) technologies. This special issue presents a collection of research that explores the performance, design, and evaluation of IOLs. From established designs and the impact of key optical parameters to innovative approaches and preoperative simulations, these contributions offer a comprehensive view of current trends and future directions in IOL development. The special issue also honors the legacy of Prof. Jim Schwiegerling whose contributions to visual optics in general, and IOLs in particular, have had a tremendous impact in the field, both in the academic, clinical and industrial communities.

Introduction to the feature Issue "Improving Vision through Intraocular Lenses": a tribute to Jim Schwiegerling

Cataract surgery, a transformative procedure to restore vision, has seen remarkable advancements in intraocular lens (IOL) technologies. This special issue presents a collection of research that explores the performance, design, and evaluation of IOLs. From established designs and the impact of key optical parameters to innovative approaches and preoperative simulations, these contributions offer a comprehensive view of current trends and future directions in IOL development. The special issue also honors the legacy of Prof. Jim Schwiegerling whose contributions to visual optics in general, and IOLs in particular, have had a tremendous impact in the field, both in the academic, clinical and industrial communities.

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 IOLs. 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 statistically significantly lower estimation errors compared to a simple linear correlation method, reducing the estimation error by approximately 6%. These findings highlight the potential of this approach to enhance the accuracy of postoperative predictions. Further work is needed to examine the potential for such postoperative predictions to improve visual outcomes in cataract patients.