Optimizing Refractive Outcomes of SMILE: Artificial Intelligence versus Conventional State-of-the-Art Nomograms.

Purpose: AI (artificial intelligence)-based methodologies have become established tools for researchers and physicians in the entire field of ophthalmology. However, the potential of AI to optimize the refractive outcome of keratorefractive surgery by means of machine learning (ML)-based nomograms has not been exhausted yet. In this study, we wanted to comprehensively compare state-of-the-art conventional nomograms for Small-Incision-Lenticule-Extraction (SMILE) with a novel ML-based nomogram regarding both their spherical and astigmatic predictability.

Determinants of Subjective Quality of Vision After Phakic Intraocular Lens Implantation.

Purpose: To evaluate postoperative subjective quality of vision in patients who underwent Implantable Collamer Lens (ICL) (STAAR Surgical) implantation for correction of myopia and to identify potential predictive parameters.

Methods: In this single-center cross-sectional study, a total of 162 eyes of 81 patients (58 women, 23 men) who underwent ICL implantation were analyzed. The Quality of Vision (QOV) questionnaire was used to assess patient-reported outcomes.