Abstract: Aspects of the present invention may include systems and methods for intraocular lens selection using a formula and a deep learning machine to estimate the error of the formula. In an aspect, the disclosure provides a method for intraocular lens selection. The method may include obtaining at least two ocular measurement parameters and a lens selection parameter for an eye. The method may include determining an intraocular lens power based on a formula using the at least two ocular measurement parameters. The method may include determining an estimated error of the formula using a deep learning machine trained on verified post-operative results using intraocular lenses selected by the formula. The method may include adjusting the lens selection parameter based on the estimated. The method may include redetermining the intraocular lens power based on the formula and the adjusted lens selection parameter.

Abstract: Aspects of the present invention may include systems and methods for intraocular lens selection using a formula and a deep learning machine to estimate the error of the formula. In an aspect, the disclosure provides a method for intraocular lens selection. The method may include obtaining at least two ocular measurement parameters and a lens selection parameter for an eye. The method may include determining an intraocular lens power based on a formula using the at least two ocular measurement parameters. The method may include determining an estimated error of the formula using a deep learning machine trained on verified post-operative results using intraocular lenses selected by the formula. The method may include adjusting the lens selection parameter based on the estimated. The method may include redetermining the intraocular lens power based on the formula and the adjusted lens selection parameter.

Abstract: The disclosure provides methods and apparatuses for determining a laser parameter set for corneal refractive surgery. The apparatus may include an autorefractor configured to obtain at least two ocular measurement parameters for an eye and to obtain a post-operative refraction of the eye. The apparatus may include a user interface configured to obtain a target refraction for the eye. The apparatus may include a memory and a processor communicatively coupled to the user interface, the autorefractor, and the memory. The processor may be configured to determine the laser parameter set based on an algorithm using the at least two ocular measurement parameters. The processor may be configured to correlate the at least two ocular measurement parameters, the laser parameter set, and the post-operative refraction as a training set.

Abstract: The disclosure provides for an apparatus for intraocular lens selection. The apparatus may include a biometer and an autorefractor. The biometer may be configured to obtain at least two ocular measurement parameters for an eye. The autorefractor may be configured to obtain a post-operative refraction of the eye. The apparatus may also include a user interface configured to obtain a lens selection parameter for the eye, a memory, and a processor communicatively coupled to the biometer, the user interface, the autorefractor, and the memory. The processor may be configured to determine an intraocular lens power based on a formula using the at least two ocular measurement parameters. The processor may be configured to correlate the at least two ocular measurement parameters, the intraocular lens power, and the post-operative refraction as a training set.

Abstract: Aspects of the present invention may include systems and methods for intraocular lens selection using a formula and a deep learning machine to estimate the error of the formula. In an aspect, the disclosure provides a method for intraocular lens selection. The method may include obtaining at least two ocular measurement parameters and a lens selection parameter for an eye. The method may include determining an intraocular lens power based on a formula using the at least two ocular measurement parameters. The method may include determining an estimated error of the formula using a deep learning machine trained on verified post-operative results using intraocular lenses selected by the formula. The method may include adjusting the lens selection parameter based on the estimated. The method may include redetermining the intraocular lens power based on the formula and the adjusted lens selection parameter.