Intraocular Lenses – Machine Learning Aids Selection

Scientists create computer models of the patient’s eyes to choose the best intraocular lenses and visual simulators for patients to experience what they would see with them.

Researchers built computational eye models using the corneas of patients who had undergone LASIK surgery to understand better how conventional intraocular lenses and lenses intended to improve depth of focus functioned in surgically repaired eyes. Computational models that utilize the patient’s eye’s anatomical data give surgeons crucial direction on the anticipated optical quality following surgery.

The sole pre-operative measurements utilized to choose the lens are the cornea’s length and curvature. With this revolutionary technology, the eye may be rebuilt in three dimensions, including the topography of the crystalline lens and cornea, which house the intraocular lens. Access to this wealth of three-dimensional data puts you in a far better position to choose the lens that will yield the highest quality image at the retinal plane.

Using techniques for optical coherence tomography quantification that they have created, researchers are undertaking a wider investigation to quantify the eye images in three dimensions to identify broader trends. They utilize machine learning techniques to identify correlations between pre- and post-operation data and provide parameters to guide the best results. Furthermore, they have created technology that enables patients to view several lens selections for themselves.

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