Predictive apparatus for assisting a physician during ophthalmic surgery

1. A method for assisting a physician in performing an ophthalmic surgery comprising:

a computational model generation step, comprising:

setting an intraocular pressure of a patient's eye to a plurality of intraocular pressures;

acquiring a plurality of quasi-real time images at the plurality of intraocular pressures at a plurality of regions of a retina of an eye, wherein the plurality of intraocular pressures results in a varied degree in distortions in the plurality of quasi-real time images and an indication of a varied degree of stress concentrations in the retina;

determining a stress level for a plurality of regions based on the plurality of quasi-real time images, a first portion of the plurality of regions having a higher stress than a second portion of the plurality of regions; and

generating a computational model of at least a portion of the patient's retinal tissue, the computational model including an indication of how the portion of the patient's retinal tissue will behave due to the stress levels for the plurality of regions; and a surgical recommendation step, comprising:

iteratively determining a recommended next region to be the first portion and a recommended next procedure to be treatment of epiretinal membrane (ERM) based on the quasi-real time image and the computational model of the eye and calculating an expected next result for the recommended next procedure to be a release in stress on a retinal tissue caused by the ERM using the quasi-real time image and the computational model; and

displaying, on a graphical display, the recommended next region, the recommended next procedure and the expected result to the physician.

2. The method of claim 1 further comprising:

receiving an initial image of at least a second portion of the eye including the operating field, the initial image including an initial region for an initial procedure;

calculating an initial expected result for the initial procedure using the initial image; and

providing the initial expected result to the physician.

3. The method of claim 1 further comprising:

iteratively repeating the receiving, determining, calculating and providing steps after the physician performs at least one procedure.

4. The method of claim 1 wherein the quasi-real time image includes at least one of an optical coherence tomograph, an ultrasound image, a high frequency ultrasound image, a ultrasound biomicroscopy (UBM) image and a three-dimensional image.

5. The method of claim 1 further comprising:

capturing the plurality of quasi-real time images.

6. The method of claim 5 wherein the step of capturing the quasi-real time image takes not more than thirty minutes.

7. The method of claim 5 wherein the step of capturing the quasi-real time image takes not more than ten minutes.

8. The method of claim 5 wherein the step of capturing the quasi-real time image takes not more than one minute.

9. The method of claim 5 wherein the step of capturing the quasi-real time image further includes:

indicating the first portion of the plurality of regions and the second portion of the plurality of regions.

10. The method of claim 1 wherein the computational model includes mechanical properties of retinal tissue of the eye.

11. The method of claim 1 wherein the recommended next procedure includes a recommended cut of the ERM.

12. A system for assisting a physician in performing ophthalmic surgery comprising:

a controller configured to set an intraocular pressure of a patient's eye to a plurality of intraocular pressures;

a quasi real-time image capture unit to acquire a plurality of quasi-real time images at a plurality of intraocular pressures at a plurality of regions of an eye, wherein the plurality of intraocular pressures results in a varied degree in distortions in the plurality of quasi-real time images and an indication of a varied degree of stress concentrations in the retina;

a predictive unit for:

determining a stress level for a plurality of regions based on the plurality of quasi-real time images, a first portion of the plurality of regions having a higher stress than a second portion of the plurality of regions;

generating a computational model of at least a portion of the patient's retinal tissue, the computational model including an indication of how the portion of the patient's retinal tissue will behave due to the stress levels for the plurality of regions; and

iteratively determining a recommended next region to be the first portion and a recommended next procedure to be treatment of epiretinal membrane (ERM) based on the quasi-real time image and a computational model of the eye and calculating an expected next result for the recommended next procedure to be a release in stress on a retinal tissue caused by the ERM using the quasi-real time image and the computational model; and

a user interface for displaying the recommended next region, the recommended next procedure and the expected result to the physician.

13. The system of claim 12 wherein

the quasi-real time image capture unit also provides an initial image of at least a second portion of the eye including the operating field, the initial image including an initial region for an initial procedure; and

the predictive unit further calculates an initial expected result for the initial procedure using the initial image and provides the initial expected result to the physician.

14. The system of claim 12 wherein the quasi-real time image includes at least one of an optical coherence tomograph, an ultrasound image, a high frequency ultrasound image, a ultrasound biomicroscopy (UBM) image and a three-dimensional image.

15. The system of claim 12 wherein the quasi-real time image is captured in not more than one minute.

16. The system of claim 12 wherein the computational model includes mechanical properties of retinal tissue of the eye.