Systems and methods for intraocular lens selection

1. A method comprising:

receiving, by one or more computing devices implementing a prediction engine, one or more pre-operative multi-dimensional images of an eye;

extracting, by the prediction engine, one or more pre-operative measurements of the eye based on the one or more pre-operative images of the eye, wherein the one or more pre-operative measurements of the eye at least include an estimated position of the lens equator determined as an equator line between each of two intersection points of an anterior lens radius and a posterior lens radius, an angle-to-angle width of an anterior chamber of the eye, an angle between a pupillary axis of the eye and a line of sight axis of the eye when vision of the eye is fixated on a fixation point, and a refractive power of a cornea of the eye;

estimating, by the prediction engine using a first prediction model based on a machine learning strategy, a post-operative position of an intraocular lens based on the one or more extracted pre-operative measurements of the eye;

selecting a power of the intraocular lens based on at least the estimated post-operative position of the intraocular lens; and

selecting the intraocular lens based on at least the selected power.

2. The method of claim 1 , further comprising:

receiving one or more post-operative multi-dimensional images of the eye after implantation of the selected intraocular lens;

extracting one or more post-operative measurements of the eye; and

updating the first prediction model based on the one or more pre-operative measurements and the one or more post-operative measurements.

3. The method of claim 1 , wherein the first prediction model comprises a neural network.

4. The method of claim 1 , wherein the post-operative position of the intraocular lens is an anterior corneal depth (ACD) of the intraocular lens or a position of an equator of the intraocular lens.

5. The method of claim 1 , wherein selecting the intraocular lens is further based on one or more of the estimated post-operative position of the intraocular lens or a diameter of a pre-operative lens.

6. The method of claim 1 , wherein the one or more pre-operative measurements include one or more of a group consisting of:

a depth of a pupillary plane;

a diameter of a pre-operative lens of the eye; and

a pre-operative anterior chamber depth of the eye.

7. The method of claim 1 , wherein selecting the power comprises recommending, by the prediction engine using a second prediction model, a recommended power for the intraocular lens based on at least the estimated post-operative position.

8. The method of claim 7 , further comprising updating the second prediction model based on the recommended power and the selected power.

9. The method of claim 1 , further comprising estimating, by the prediction engine using a second prediction model, a post-operative manifest refraction spherical equivalent (MRSE) based on at least the estimated post-operative position and the selected power.

10. The method of claim 9 , further comprising:

measuring an actual power-operative MRSE; and

updating the second prediction model based on the estimated post-operative MRSE and the actual power-operative MRSE.

11. The method of claim 1 , wherein the one or more pre-operative multi-dimensional images are each received from a diagnostic device selected from a group consisting of an optical coherence tomography (OCT) device, a rotating Scheimpflug camera, and a magnetic resonance imaging (MRI) device.

12. The method of claim 1 , further comprising planning one or more procedures for implantation of the intraocular lens.

13. The method of claim 1 , further comprising displaying one of the one or more pre-operative multi-dimensional images annotated with one of the one or more extracted pre-operative measurements.

14. A prediction engine comprising:

one or more processors;

wherein the prediction engine is configured to:

receive one or more pre-operative multi-dimensional images of an eye obtained by a diagnostic device;

extract one or more pre-operative measurements of the eye based on the one or more pre-operative images of the eye, wherein the one or more pre-operative measurements of the eye at least include an estimated position of the lens equator determined as an equator line between each of two intersection points of an anterior lens radius and a posterior lens radius, an angle-to-angle width of an anterior chamber of the eye, an angle between a pupillary axis of the eye and a line of sight axis of the eye when vision of the eye is fixated on a fixation point, and a refractive power of a cornea of the eye;

estimate, using a first prediction model based on a machine learning strategy, a post-operative position of an intraocular lens based on the one or more extracted pre-operative measurements of the eye;

recommend a power of the intraocular lens based on at least the estimated post-operative position of the intraocular lens; and

provide the recommended power to a user to facilitate selection of the intraocular lens for implantation.

15. The prediction engine of claim 14 , wherein the prediction engine is further configured to:

receive one or more post-operative multi-dimensional images of the eye after implantation of the selected intraocular lens;

extract one or more post-operative measurements of the eye; and

update the first prediction model based on the one or more pre-operative measurements and the one or more post-operative measurements.

16. The prediction engine of claim 14 , wherein the post-operative position of the intraocular lens is an anterior corneal depth (ACD) of the intraocular lens or a position of an equator of the intraocular lens.

17. The prediction engine of claim 14 , wherein the one or more pre-operative measurements include one or more of a group consisting of:

a depth of a pupillary plane;

a diameter of a pre-operative lens of the eye;

a pre-operative anterior chamber depth of the eye.

18. The prediction engine of claim 14 , further comprising estimating, by the prediction engine using a second prediction model, a post-operative manifest refraction spherical equivalent (MRSE) based on at least the estimated post-operative position and a selected power of the intraocular lens.

19. A non-transitory machine-readable medium comprising a plurality of machine-readable instructions which when executed by one or more processors are adapted to cause the one or more processors to perform a method comprising:

receiving one or more pre-operative multi-dimensional images of an eye;

extracting one or more pre-operative measurements of the eye based on the one or more pre-operative images of the eye, wherein the one or more pre-operative measurements of the eye at least include an estimated position of the lens equator determined as an equator line between each of two intersection points of an anterior lens radius and a posterior lens radius, an angle-to-angle width of an anterior chamber of the eye, an angle between a pupillary axis of the eye and a line of sight axis of the eye when vision of the eye is fixated on a fixation point, and a refractive power of a cornea of the eye;

estimating, using a prediction model based on a machine learning strategy, a post-operative position of an intraocular lens based on the one or more extracted pre-operative measurements of the eye;

recommending a power of the intraocular lens based on at least the estimated post-operative position of the intraocular lens; and

providing the recommended power to a user to facilitate selection of the intraocular lens for implantation.

20. The non-transitory machine-readable medium of claim 19 , wherein the method further comprises:

receiving one or more post-operative multi-dimensional images of the eye after implantation of the selected intraocular lens;

extracting one or more post-operative measurements of the eye; and

updating the prediction model based on the one or more pre-operative measurements and the one or more post-operative measurements.