The Use of Performance-based Metrics to Design Corrections and Predict Outcomes

Transcription

1 The Use of Performance-based Metrics to Design Corrections and Predict Outcomes Jason D. Marsack Visual Optics Institute, College of Optometry, University of Houston 1/28/6 1: 1:2 am

2 Visual Optics Institute Current members of the Applegate laboratory Top row: Jason Marsack, Darren Koenig, Jim Elswick, Raymond Applegate Bottom row: Katrina Parker, Yafei Niu, Michelle Koe, William Donnelly

3 Capability for Wavefront-Guided Corrections Lasers (and lathes) are capable of cutting high order aberration corrections. The question is not whether we can implement customized high order corrections, but what aberrations should be corrected. A strategy that minimizes the complexity of the correction while providing excellent visual performance would be optimal.

4 The Use of Performance-based Metrics to Design Corrections and Predict Outcomes Definition of Terms 1. Corrections: custom refractive corrections, taking both higher and lower order aberrations into account. In the examples to follow, contact lens corrections are examined. 2. Predict outcomes: build these corrections in a prospective manner, requiring fewer iterations to reach maximized visual performance. 3. Performance-based metrics: a mathematical transform of wavefront data that can be correlated to a visual performance measure.

5 What is a single value metric? Zernike Coefficients metric calculator Single value pertaining to some property of the wavefront 1. Hong X, Himebaugh N, Thibos LN. On-eye evaluation of optical performance of rigid and soft contact lenses. Optom Vis Sci 21;78: Applegate RA, Ballentine C, Gross H, Sarver EJ, Sarver CA. Visual acuity as a function of Zernike mode and level of root mean square error. Optom Vis Sci 23;8: Thibos LN, Hong X, Bradley A, Applegate RA. Accuracy and precision of objective refraction from wavefront aberrations. J Vis 24;4: Cheng X, Bradley A, Thibos LN. Predicting subjective judgment of best focus with objective image quality metrics. J Vis 24;4: Marsack JD, Thibos LN, Applegate RA. Metrics of optical quality derived from wave aberrations predict visual performance. J Vis 24;4: Chen L, Singer B, Guirao A, Porter J, Williams DR. Image metrics for predicting subjective image quality. Optom Vis Sci 25;82:

6 What is a single value metric? Example of Metric Calculation Zernike coefficients th order 1 st order 2 nd order _C(n) 2 n RMS rd order

7 What is a single value metric? Metrics Reduce Data and Convey Some Aspect of the Wavefront Zernike coefficients th order 1 st order 2 nd order 3 rd order RMS 3.21

8 What is a single value metric? Metrics Reduce Data and Convey Some Aspect of the Wavefront Zernike coefficients th order 1 st order 2 nd order 3 rd order PV 15.87

9 Identify metrics that are predictive of performance Individual performance values Calculate metric values for similar individuals Record the performance metric for those individuals Examine how well the metric explains performance by creation of model individual metric values Log LIB is predictive of PRCS in keratoconus subjects.

10 What makes a metric good at predicting visual performance 1. Metrics will likely vary in their ability to predict depending on the cohort of subjects under study. LIB has been shown to be a good predictor of performance for keratoconus subjects. LIB has been shown to be a poor predictor of performance for normal subjects. 2. Metrics will likely vary in their ability to predict depending on the task. RMS has been shown to be a poor predictor of VA in normals Visual strehl has been shown to be a good predictor of VA in normals. A good metric predicts the visual performance measure for the cohort of interest.

11 How might metrics be used to evaluate refractive corrections Evaluation of a correction for an individual: 1. Identify a database of similar individuals with previously collected wavefront aberration and visual performance measures. 2. Generate predictive model. 3. Measure wavefront in patient in the presence of the correction of interest. 4. Calculate metric value for patient. 5. Does the model predict the actual visual performance?

12 Example: Predicting RGP performance in keratoconus A dataset consisting of simulated optical corrections and VA measures for keratoconus subjects was used to build the predictive model (Marsack et Al. JOSA A in press). The ability of the model to predict the VA of real keratoconus subjects corrected with RGPs was evaluated. Apply correction factor if necessary

13 How might metrics be used to design refractive corrections Prospective design of a correction for an individual: 1. Identify a database of similar individuals with previously collected wavefront aberration and visual performance measures. 2. Generate predictive model. 3. Identify target performance value from model. 4. Identify required metric value. 5. Measure wavefront in patient of interest. 6. Iteratively study numerous combinations of wavefront corrections and calculate metric value. 7. Choose correction from those that reach the required metric value.

14 How might metrics be used to design refractive corrections Target PRCS value: 1.75 Target metric value: -1. Zernike coefficients th order 1 st order 2 nd order 3 rd order Correction description 2 nd order 3 rd order 4 th order 5 th order Metric value Manufacture and test 3 rd or 4 th order corrections

15 Conclusions There are, theoretically, an infinite number of metrics that can be calculated from wavefront data. These metrics describe some aspect of the wavefront error of the eye. The metrics can be correlated with visual performance measures. If correlations are strong, the metric values can be used to predict visual performance under a variety of correction levels. This is shown here by predicting visual performance in RGP corrected keratoconus subjects. Future Directions Build corrections based on metric predictions of visual performance Validate that visual performance is improved.

16 Acknowledgements People: Dr. Raymond Applegate Dr. Harold Bedell Dr. Larry Thibos Dr. Austin Roorda Dr. Susana Chung Dr. Earl Smith Dr. Konrad Pesudovs Dr. Laura Frishman William Donnelly III Dr. William Miller Dr. Edwin Sarver Wavefront Sciences Dr. Krishna Venkateshwaran Dr. Katrina Parker DAC International Dr. Norman Leach Dr. Alison McDermott UT BME faculty, staff & students Niki Bedell Laura Johnson All UHCO faculty, staff & students Dr. Dawn Lam Dr. Robin Bynum TERTC Dr. Jan Bergmanson Financial Support: NIH/NEI EY7551 Core Grant, instrumentation module to UHCO. AOF-Vistakon soft contact lens research grant to JDM. NIH/NEI EY724 Joint Training Grant to JDM. NIH/NEI EY528 to RAA Ezell Fellowship to JDM Thank you and questions