Cataract surgery is one of the most commonly performed surgeries worldwide and has a high postoperative patient satisfaction rate.~Despite the significant improvement in measurement methods and artificial lens power calculation formulas, miscalculations and subsequently refractive error still occur after cataract surgery.~Most current artificial lens calculation methods use regression formulas. The concept of artificial intelligence (AI) has only recently found use and only in a rudimentary way. Examples include the Hill-RBF calculator and the Ladas superformula, but at least the superformula does not use a true AI approach and the results are no better than those obtained with conventional methods.~Study Purpose:~The main purpose of the study is to develop an AI approach to improve intraocular lens power calculations. The data will be used for the following analyses:~To compare the new AI method with conventional and modern formula (main question).~To compare the refractive error for different eye lengths.~To use a ray tracing method to quantify sources of error in patients with a refractive surprise of at least 1.0 diopter~To evaluate a novel concept of outlier testing using a modified nonparametric Walsh test~Study Design:~This study will be multi-center and partially prospective. We plan to analyze data from 1000 patients who have already had cataract surgery.~All patients will be invited for a visit to the recruitment clinic and the following measurements (optical biometry and subjective/autorefraction + visual acuity) will be performed. All of these measurements are non-contact. Furthermore, no eye drops need to be used.~If a patient is not willing to attend the recruitment clinic, this will be documented and another patient will be called instead to fulfill the data set of 1000.
Study Purpose:~The main purpose of the study is to develop an AI approach to improve intraocular lens power calculations.