A PERIMETRIC RE-TEST ALGORITHM THAT IS SIGNIFICANTLY MORE ACCURATE THAN CURRENT PROCEDURES Andrew Turpin School of Computer Science and Information Technology RMIT University, Melbourne Darko Jankovic Department of Optometry and Vision Science University of Melbourne Allison McKendrick Department of Optometry and Vision Science University of Melbourne

Possible re-test algorithms 1. Use individual presentation information… 2. Use test Hill of Vision to bias re-test 3. Continue the previous test with “next” termination criteria –More reversals (staircase, MOBS) –Tighter PDF standard deviation (Bayesian) –Fixed number of presentaitons 4. Use test thresholds to seed re-test –Starting point for staircase (FT From Prior) –Initialisation of MOBS stacks –Centre a PDF around threshold (ZEST, SITA)

Prior distribution (before first presentation)

After 1 presentation

After 2 presentations

After 3 presentations

After 4 presentations

After 5 presentations Gaussian with standard deviation 3dB

1.Continued ZEST Termination Criteria -Fixed # presentations 4,5,6 -Standard deviation 0.7, 0.8, 0.9, 1.0 LF -Steep, steeper, steepest 2.Seeded ZEST PDFs -Gaussian standard deviation 2,3,4 dB -Step function, width 4,6,8,10 dB LF -Steep, steeper, steepest Termination criteria -Fixed # presentations 4,5,6 -Standard deviation 0.7, 0.8, 0.9, MOBS 1.Stack initialisation 2, 3, 4 dB 2.Termination criteria: 2, 3 reversals 2, 3, 4 width 486 procedure s Patient setFalse + False - Normal-10% Normal-23%15% Normal-315%3% Normal-420% Glaucoma-10% Glaucoma-23%15% Glaucoma-315%3% Glaucoma-420% 8 Patient models Computer Simulations 350 real patients

Performance: No Error, No Change 4567 Mean number of presentations Mean absolute error (dB) 2 1 Z F ull Threshold est S eeded Zest C ontinued Zest S ITA Bengtsson et al, ACTA ‘97

Performance: General Height -3dB 4567 Mean absolute error (dB) 2 1 C S Z F ull Threshold est eeded Zest ontinued Zest S ITA Mean number of presentations

Problems Continue General Height change ignored, need many presentations to get right answer if GH changes, and there is still a bias towards original test value Seed Could adjust seed if GH change known –Estimate with “primary points” algorithm –Would be slower than Full Threshold (and SITA) Katz et al, IOVS 1632

Speeding up GH-corrected Seed Spend 2 or 3 presentations per location checking if threshold not less than last time (multi-sample supra-threshold) If so, then do no more for that location Otherwise, assume threshold decreased, and seed a ZEST accordingly McKendrick & Turpin, OVS 2005

… … Automated Static Perimetry, 1999, Anderson & Patella

General Height decrease of 2dB Supra-threshold decrement of 2dB So multi-sample all locations at previous less 4dB If see this 2 of 3 times, then just use previous threshold - 2dB else do a full ZEST on the location TestRe-Test

General Height decrease of 2dB Supra-threshold decrement of 2dB So test all locations at previous less 4dB If see this 2 of 3 times, then just use previous threshold - 2dB else do a full ZEST on the location

Performance with no error 4567 Mean absolute error (dB) 2 1 C SZ F ull Threshold esteeded Zest ontinued Zest N ew S ITA Mean number of presentations

General height -3dB 4567 Mean absolute error (dB) 2 1 C S Z F ull Threshold est eeded Zest ontinued Zest N ew S ITA Mean number of presentations

Conclusions Continuing previous procedure doesn’t work Seeding a ZEST with a Gaussian pdf about previous threshold works, but is slow Adding multi-sampling supra-threshold step gives speed and accuracy gains The resulting re-test procedure is as fast, but more accurate, than existing test algorithms BUT does not detect an isolated increase in threshold

Hill of Vision Approach Alter eccentricity adjustments in growth pattern based on individual’s HoV Takes into account General Height change Very small gains, but not really worth the effort

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After 6 presentations

After 7 presentations

After 8 presentations

After 9 presentations