PHYSICALLY MOTIVATED MACHINE LEARNING SYSTEM FOR OPTIMIZED INTRAOCULAR LENS CALCULATION

摘要

A computer-implemented method for determining the refractive power for an intraocular lens to be used is presented. The method includes generating first training data for a machine learning system based on a first physical model for a refractive power for an intraocular lens and training the machine learning system using the generated first training data to form a first learning model for determining refractive power. Furthermore, the method includes training the machine learning system trained with the first training data with clinical ophthalmological training data to form a second learning model for determining the refractive power and providing ophthalmological data of a patient and an expected position of the intraocular lens to be inserted. In addition, the method has a prediction of the refractive power of the intraocular lens to be used by means of the trained machine learning system and the second learning model. The provided ophthalmological data and the position of the intraocular lens are used as input values for the machine learning system with the second learning model.