System and method for Optimizing clinical optic prescriptions

• US 20070115432A1
• Filed: 12/13/2004
• Published: 05/24/2007
• Est. Priority Date: 12/12/2003
• Status: Active Grant

• Current Assignee: Indiana University Research & Technology Corporation (Indiana University), University of Houston (University of Houston System)
• Original Assignee: University of Houston (University of Houston System)
• Inventors: Thibos, Larry
• Granted Patent: US 7,857,451 B2
• Time in Patent Office: Days
• US Class Current: 351/246
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

An method and system for determining the appropriate refractive prescription in a clinical optometry or ophthalmology setting. Data in the form of aberrometric input, patient history and other information, and/or other environmental data is used to optimize a real-world prescription for an individual'"'"'s optic needs through the use of a equivalent quadratic fitting calculation or a simulated through focus experiment.

Claims

1. A method for optimizing a refractive prescription, the method comprising the steps of:

a. obtaining aberrometric data from a patient by way of an aberrometer; and

b. utilizing the aberrometric data to perform a equivalent quadratic fitting calculation to obtain at least one clinical refractive prescription for the patient.

2. The method of claim 1, further comprising the step of adjusting the refractive prescription to maximize the utilization of the patient'"'"'s depth of field if the aberrometric data suggests that the patient'"'"'s vision is myopic.

3. The method of claim 1, further comprising the step of adjusting the ideal optic prescription to maximize the utilization of the patient'"'"'s depth of field if the aberrometric data suggests that the patient'"'"'s vision is hyperopic.

4. The method of claim 1, wherein the step of utilizing the aberrometric data to perform a equivalent quadratic fitting calculation is performed by a computer processor.

5. The method of claim 1, further comprising the step of evaluating the results and allowing a user to determine whether the prescription should be further optimized.

6. The method of claim 1, further comprising the step of selecting one of a plurality of optic prescriptions.

7. The method of claim 1, further comprising the steps of:

a. obtaining patient data; and

b. utilizing the patient data to optimize a clinical refractive prescription.

8. The method of claim 1, further comprising the steps of:

a. obtaining environmental data; and

b. utilizing the patient data to optimize a clinical refractive prescription.

9. A method for optimizing a refractive prescription, the method comprising the steps of:

a. obtaining aberrometric data from a patient by way of an aberrometer;

b. selecting a metric of image quality;

c. generating an aberration map from the aberrometric data; and

d. simulating a through focus experiment.

10. The method of claim 9, further comprising the step of adjusting the ideal optic prescription to maximize the utilization of the eye'"'"'s depth of field if the aberrometric data suggests that the patient'"'"'s vision is myopic.

11. The method of claim 9, further comprising the step of adjusting the ideal optic prescription to maximize the utilization of the eye'"'"'s depth of field if the aberrometric data suggests that the patient'"'"'s vision is hyperopic.

12. The method of claim 9, wherein the step of simulating a through focus experiment is performed by a computer processor.

13. The method of claim 9, further comprising the step of evaluating the results and allowing a user to determine whether the prescription should be further optimized.

14. The method of claim 9, further comprising the steps of:

a. obtaining patient data; and

b. utilizing the patient data to optimize a clinical refractive prescription.

15. The method of claim 9, further comprising the steps of:

a. obtaining environmental data; and

b. utilizing the patient data to optimize a clinical refractive prescription.

16. The method of claim 9, comprising the additional step of recalculating metrics for each condition in the through focus simulation.

17. The method of claim 16, further comprising the step of selecting a prescription that maximizes the chosen metric.

18. The method of claim 17, wherein the prescription that maximizes the chosen metric is maximized for a specific distance.

19. The method of claim 17, wherein the prescription that maximizes the chosen metric is maximized to achieve a desired trade-off between maximal quality and depth of focus.