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6.
Determine the rate of progression Simulated data. to identify patients at risk.
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Assess treatment effectiveness Simulated data. by comparing rates before and after treatment. Before : -3.9 microns/yr ± 1.1 After : -1.8 microns/yr ± 1.1
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<ul><li>Minimum cluster size is 150 pixels (2% of image area) </li></ul><ul><li>95% design specificity </li></ul>Simulated data. Image Progression Map: For deep, narrow focal defects
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<ul><li>Divides ring around optic nerve into 64 segments </li></ul><ul><li>3 adjacent segments must show change. </li></ul><ul><li>95% design specificity </li></ul>Simulated data. TSNIT Progression Graph: For broader, shallow focal defects
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<ul><li>Regression line drawn for Likely Progression and p<5% </li></ul><ul><li>95% design specificity </li></ul><ul><li>TSNIT Average, Superior Average, and Inferior Average </li></ul>Simulated data. Parameter Progression Charts: For diffuse defects
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Courtesy of Robert N. Weinreb, MD Felipe A. Medeiros, MD, PhD Hamilton Glaucoma Center University of California at San Diego GDx Progression: Case Examples
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<ul><li>Deep, narrow wedge defect (~10 degrees) </li></ul><ul><li>Only identified by Image Progression Map </li></ul>Data presented using research format. Example 1: deep, narrow focal defect
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<ul><li>Shallow inferior defect </li></ul><ul><li>Only identified by TSNIT Progression Graph </li></ul>Data presented using research format. Example 2: shallow, broad focal defect
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<ul><li>Inferior defect </li></ul><ul><li>Sufficient area and depth to be identified by all three approaches </li></ul>Data presented using research format. Example 3: larger defect identified by all
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GPA TM tools integrate your structure and function analysis by using consistent terms and rules. Extending GPA TM across structure and function
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GPA TM Progression for GDx <ul><li>GPA ™ Progression Analysis for GDx is a comprehensive, simple tool to help you: </li></ul><ul><li>Make more confident treatment decisions </li></ul><ul><li>Effectively educate patients </li></ul>Summary