Abstract: Intraocular lens (IOL) designs include having an optic with an anterior surface and a posterior surface surrounded by an optic edge. In some examples, the IOL has a plurality of haptics, each attached to the optic at a gusset, where each gusset extends beyond the optic edge toward an optic center such that the gusset at least partially overlaps with the anterior surface of the optic. In some examples, the IOL includes a ring structure integral with the optic and surrounding the perimeter of the optic edge, the ring structure having a thickness and the optic edge having a thickness, the ring structure thickness greater than the optic edge thickness.

Abstract: A system for selecting a preferred intraocular lens for implantation into an eye includes a controller having a processor and a tangible, non-transitory memory. The controller is configured to obtain diagnostic data of the eye, and obtain historical data composed of historical sets of patient data. The controller is configured to analyze individual risk factors based on the diagnostic data and obtain a weighted combination of the individual risk factors. A respective satisfaction metric for the plurality of intraocular lenses is generated based on the historical data. A preferred intraocular lens may be selected based in part on the respective satisfaction metric and the weighted combination. A visual simulation for each of the plurality of intraocular lenses may be performed, based in part on the diagnostic data. The visual simulation may incorporate an impact of the tear film data.

Abstract: Certain aspects of the present disclosure provide techniques for performing surgical ophthalmic procedures, such as cataract surgeries. An example method generally includes generating, using one or more measurement devices, one or more data points associated with measurements of one or more anatomical parameters for an eye to be treated. Using one or more trained machine learning models, one or more recommendations are generated including one or more IOL parameters for the IOL to be used in the cataract surgery based, at least in part, on the one or more data points. The machine learning models are trained based on at least one historical data set of data points associated with measurements of anatomical parameters mapped to treatment data and treatment result data associated with each historical patient. The one or more IOL parameters comprise one or more of an IOL type, an IOL power, or IOL placement information for implanting the IOL in the eye.

Abstract: An ophthalmic system for generating an ocular model of an eye includes an optical coherence tomography (OCT) device, an aberrometer, and a computer. The OCT device detects OCT light reflected from the eye. The aberrometer detects aberrometer light reflected from the eye. The computer generates the ocular model of the eye according to the reflected OCT light. The ocular model includes parameters describing the eye. The parameters include lens parameters that describe the lens of the eye. The computer determines an OCT-based wavefront according to the ocular model, determines an aberrometer-based wavefront according to the reflected aberrometer light, and compares the OCT-based and the aberrometer-based wavefronts. If the wavefronts differ beyond a predefined tolerance, the computer adjusts one or more values assigned to the parameters until the wavefronts satisfy the predefined tolerance. At least one adjusted value is assigned to a lens parameter.

Abstract: An ophthalmic system for measuring an eye comprises measuring devices and a computer. The measuring devices comprise an optical coherence tomography (OCT) device and an aberrometer. The OCT device directs OCT light towards the eye, and detects the OCT light reflected from the eye to measure the eye. The aberrometer directs aberrometer light towards the eye, and detects the aberrometer light reflected from the eye to measure the eye. The computer generates an ocular model of the eye according to the reflected OCT light. The ocular model comprises parameters for the eye, where each parameter is assigned a value. The computer determines an OCT-based wavefront according to the ocular model, determines an aberrometer-based wavefront according to the reflected aberrometer light, ascertains a deviation between the OCT-based wavefront and the aberrometer-based wavefront, and evaluates measurements from one or more of the measuring devices according to the deviation.

Abstract: Certain aspects of the present disclosure provide techniques for predicting a likelihood of future failure of components in an ophthalmic medical device and performing preventative maintenance on the ophthalmic medical device. An example method generally includes receiving, from an ophthalmic medical device, measurements of one or more operational parameters associated with the ophthalmic medical device. Using one or more models, a future failure of the ophthalmic medical is predicted. The predictions are generated based, at least in part, on the received measurements of the one or more operational parameters. One or more actions are taken to perform preventative maintenance on the ophthalmic medical device based on the predicted future failure of the ophthalmic medical device.

Abstract: A method for assessing a lens capsule stability condition in an eye of a human patient includes directing electromagnetic energy in a predetermined spectrum onto a pupil of the eye, via an energy source, concurrently subsequent to a movement of the eye causing eye saccades to occur therein. The method also includes acquiring images of the eye indicative of the eye saccades using an image capture device, and computing, via the ECU, a motion curve of the lens capsule using the images. Additionally, the method includes extracting time-normalized lens capsule oscillation traces based on the motion curve via the ECU, and then model-fitting the lens capsule oscillation traces via the ECU to thereby assess the lens capsule instability condition. An automated system for performing an embodiment of the method is also disclosed herein, including the energy source, image capture device, and ECU.

Abstract: A system for predicting post-operative vignetting in an eye of a subject includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The controller is in communication with a diagnostic module adapted to store pre-operative anatomic data of the eye as an eye model. The system includes a projection module and a ray tracing module selectively executable by the controller. The projection module is adapted to determine imputed post-operative variables of the eye based at least partially on the pre-operative anatomic data and the intraocular lens. The ray tracing module is adapted to calculate propagation of light through the eye. The ray tracing module is executed to determine a light distribution for respective visual field angles across a predefined field of view. The controller is configured to determine one or more post-operative vignetting parameters based at least partially on the light distribution.

Abstract: A system and method for selecting a preferred intraocular lens, for implantation into an eye, includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The controller is in communication with a diagnostic module adapted to store pre-operative anatomic data of the eye as an eye model. The controller is configured to determine respective imputed post-operative variables for each of a plurality of intraocular lenses, via a projection module. A respective pseudophakic eye model is generated for each of the plurality of intraocular lenses by incorporating the respective imputed post-operative variables into the eye model. A ray tracing module is executed in the respective pseudophakic eye model to determine at least one respective metric for the plurality of intraocular lenses. The preferred intraocular lens is selected based on a comparison of the respective metric.

Abstract: A system and method for selecting an intraocular lens, for implantation into an eye, includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute a machine learning model trained with a training dataset. Execution of the instructions by the processor causes the controller to obtain pre-operative objective data for the patient, including one or more anatomic eye measurements. The controller is configured to obtain pre-operative questionnaire data for the patient, including at least one personality trait. The pre-operative objective data and the pre-operative questionnaire data are entered as respective inputs to the machine learning model. A predicted subjective outcome score for the patient is generated as an output of the machine learning model. The intraocular lens is selected based in part on the predicted subjective outcome score.

Abstract: Intraocular lens (IOL) designs include having an optic with an anterior surface and a posterior surface surrounded by an optic edge. In some examples, the IOL has a plurality of haptics, each attached to the optic at a gusset, where each gusset extends beyond the optic edge toward an optic center such that the gusset at least partially overlaps with the anterior surface of the optic. In some examples, the IOL includes a ring structure integral with the optic and surrounding the perimeter of the optic edge, the ring structure having a thickness and the optic edge having a thickness, the ring structure thickness greater than the optic edge thickness.

Abstract: Systems, methods, and devices for inserting an intraocular lens (IOL) assembly into an eye may be provided. An apparatus for delivery of a lens component into an eye may include a housing. The apparatus may further include a plunger at least partially disposed in the housing, wherein the housing comprises an elongated portion and a viscoelastic soft tip at a distal end of the elongated portion, wherein the viscoelastic soft tip has a storage modulus of about 1 megapascal (MPa) to about 300 MPa and a loss module of about 1 MPa to about 300 MPa. The apparatus may further include a drive mechanism operatively coupled to plunger and configured to cause the plunger to translate in the housing. The apparatus may further include a nozzle operatively coupled to the housing through which the plunger delivers the lens component into the eye.