A personalized assistance system for a user of a vision correction device includes a remote computing unit with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute one or more machine learning models. A user device is operable by the user and includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specific intervals. The electronic diary module is configured to store respective answers, entered by the user in response to the one or more preselected questions, as self-reported data. The controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data, via the one or more machine learning models. The controller is configured to assist the user based in part on the analysis.

A personalized assistance system for a user of a vision correction device includes a remote computing unit with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute one or more machine learning models. A user device is operable by the user and includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specific intervals. The electronic diary module is configured to store respective answers, entered by the user in response to the one or more preselected questions, as self-reported data. The controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data, via the one or more machine learning models. The controller is configured to assist the user based in part on the analysis.

Systems and methods for visual acuity calculation including consideration of a combination of ocular aberrations and lens aberrations are disclosed. One method includes obtaining ocular aberration data and introducing a correction in the defocus term of the ocular aberration data to account for longitudinal chromatic aberration. Lens aberration data is obtained, including performing raytracing through the ophthalmic lens of the patient. Correction to tilt and defocus terms of the lens aberration data is made to account for transverse and longitudinal chromatic aberrations. Polychromatic Point Spread Functions (PSFs) associated to the ocular aberration data and lens aberration data are used to generate retinal images. Retinal sampling is applied to the retinal images, followed by filtering and normalizing the retinal images is also performed. Finally, a maximum visual acuity value is determined. The methods are performed using one or more computing devices.

Systems and methods for visual acuity calculation including consideration of a combination of ocular aberrations and lens aberrations are disclosed. One method includes obtaining ocular aberration data and introducing a correction in the defocus term of the ocular aberration data to account for longitudinal chromatic aberration. Lens aberration data is obtained, including performing raytracing through the ophthalmic lens of the patient. Correction to tilt and defocus terms of the lens aberration data is made to account for transverse and longitudinal chromatic aberrations. Polychromatic Point Spread Functions (PSFs) associated to the ocular aberration data and lens aberration data are used to generate retinal images. Retinal sampling is applied to the retinal images, followed by filtering and normalizing the retinal images is also performed. Finally, a maximum visual acuity value is determined. The methods are performed using one or more computing devices.

A method for optimizing an ophthalmic treatment, comprising: measuring a patient's eye with an ophthalmic measurement instrument, fabricating a trial correction lens and testing it on the patient's eye, determining a score or success criteria for the trial correction, using the score or success criteria to provide training information to a machine-learning algorithm, and using the machine-learning algorithm to determine an optimal ophthalmic correction.

The invention relates to a method for determining an ophthalmic lens intended to be worn by an individual, said ophthalmic lens being adapted to provide to the individual a vision correction at at least one given vision gaze direction, said vision correction being based on wearer data including prescription data of the individual,

wherein the method comprises the steps of: determining a parameter pertaining to the accommodative dynamics of an eye of the individual, determining said ophthalmic lens based on said wearer data and on the parameter pertaining to the accommodative dynamics of an eye of the individual.

The invention also relates to a device for determining the parameter pertaining to the accommodative dynamics of an eye of an individual in the method according to the invention.

Processing a set of images is disclosed, including: determining a set of user head measurements from a set of images; and determining a fit score corresponding to a glasses frames based at least in part on comparing the set of user head measurements to glasses frame measurements associated with the glasses frames.

A visual examining and training device includes a wearing unit that is suitable for wearing on a head of a user, that is configured to be disposed in front of the eyes of the user, and that has a main housing, and two lens adjusting units spacedly disposed in the main housing and each of which includes a lens carrier having a tubular member defining an inner space, a rotary lens holder assembly disposed in the inner space, two prisms coaxially disposed in the rotary lens holder assembly, a focal length adjusting lens disposed in the inner space spaced apart from the prisms, and a drive mechanism connected to the rotary lens holder assembly for driving the same together with the prisms to rotate. A control unit is signally connected to the drive mechanisms of the lens adjusting units for controlling operation of the same.

An ophthalmic apparatus obtains naked-eye wavefront aberration data of an examinee's eye measured by an aberration measuring unit, and calculates first corrected wavefront aberration data intended for a prescription with a first correction power based on the naked-eye wavefront aberration data and the first correction power and generates a first evaluation index based on the first corrected wavefront aberration data. The ophthalmic apparatus further calculates second corrected wavefront aberration data intended for a prescription with a second correction power different from the first correction power in at least one of spherical power, astigmatic power, and astigmatic axis angle based on the naked-eye wavefront aberration data and the second correction power, and generates a second evaluation index based on the second corrected wavefront aberration data. The ophthalmic apparatus then displays the first and second evaluation indexes selectively or in parallel on a monitor.

An ophthalmic apparatus obtains naked-eye wavefront aberration data of an examinee's eye measured by an aberration measuring unit, and calculates first corrected wavefront aberration data intended for a prescription with a first correction power based on the naked-eye wavefront aberration data and the first correction power and generates a first evaluation index based on the first corrected wavefront aberration data. The ophthalmic apparatus further calculates second corrected wavefront aberration data intended for a prescription with a second correction power different from the first correction power in at least one of spherical power, astigmatic power, and astigmatic axis angle based on the naked-eye wavefront aberration data and the second correction power, and generates a second evaluation index based on the second corrected wavefront aberration data. The ophthalmic apparatus then displays the first and second evaluation indexes selectively or in parallel on a monitor.