PIF2024 - Construction of eye models and their application - (PID2023-152641OA-I00)

Abstract: Changes associated with ageing of the human eye are most noticed in the crystalline lens, that first loses its capability to accommodate (presbyopia) and later on can lose its transparency (cataract). In a cataract surgery, the crystalline lens is removed and a foldable intraocular lens (IOL) is placed into its capsule bag. Cataract surgery is a well-established technique and is generally very successful, but postoperative refractive errors are still common because current IOL power selection methods rely on inaccurate and incomplete preoperative measurements and simple linear regression formulas that fail in patients whose eye’s geometry falls outside normal population. New optical technologies mapping the full 3-D geometry of the eye prior to surgery and custom-based eye models, proposed in the PID2023-152641OA-I00 project, will allow comprehensive surgical planning and simulations of the optical performance of specific solutions in-eye, tuned to the patient’s eye anatomy. Machine learning methods can help to use patient population data to guide the IOL selection. About the thesis: The PhD student will perform experimental measurements with novel optical imaging techniques based on OCT systems for precise quantification of the ocular geometry, will develop new methods for the construction of 3-D eye models and will explore new approaches based on machine learning algorithms for improving the selection of an intraocular lens to be implanted.