a perimetric re-test algorithm that is significantly more accurate than current procedures
DESCRIPTION
A PERIMETRIC RE-TEST ALGORITHM THAT IS SIGNIFICANTLY MORE ACCURATE THAN CURRENT PROCEDURES. Allison McKendrick Department of Optometry and Vision Science University of Melbourne. Andrew Turpin School of Computer Science and Information Technology RMIT University, Melbourne. Darko Jankovic - PowerPoint PPT PresentationTRANSCRIPT
A PERIMETRIC RE-TEST ALGORITHM THAT IS SIGNIFICANTLY MORE
ACCURATE THAN CURRENT PROCEDURES
Andrew TurpinSchool of Computer Science and Information TechnologyRMIT University, Melbourne
Darko JankovicDepartment of Optometry and
Vision ScienceUniversity of Melbourne
Allison McKendrick Department of Optometry
and Vision ScienceUniversity of Melbourne
Possible re-test algorithms
1. Use individual presentation information…2. Use test Hill of Vision to bias re-test3. 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, 1.0
3. MOBS- Stack initialisation 2, 3, 4 dB- Termination criteria: 2, 3 reversals
2, 3, 4 width
486 procedure
s
Patient set False+
False-
Normal-1 0% 0%
Normal-2 3% 15%
Normal-3 15% 3%
Normal-4 20% 20%
Glaucoma-1 0% 0%
Glaucoma-2 3% 15%
Glaucoma-3 15% 3%
Glaucoma-4 20% 20%
8 Patientmodels
Computer Simulations
350 realpatients
Performance: No Error, No Change
4 5 6 7Mean number of presentations
Mea
n ab
solu
te e
rror
(dB
)
2
1Z
F ull Threshold
estS eeded ZestC ontinued Zest
S ITA
Bengtsson et al, ACTA ‘97
Performance: General Height -3dB
4 5 6 7
Mea
n ab
solu
te e
rror
(dB
)
2
1
C
S
Z
F ull Threshold
est
eeded Zest
ontinued ZestS ITA
Mean number of presentations
ProblemsContinue• 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
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 …
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Automated Static Perimetry, 1999, Anderson & Patella
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3031
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General Height decrease of 2dBSupra-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 - 2dBelse do a full ZEST on the location
27 29
Test Re-Test
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General Height decrease of 2dBSupra-threshold decrement of 2dB
So test all locations at previous less 4dB
If see this 2 of 3 times, then just use previous threshold - 2dBelse do a full ZEST on the location
Performance with no error
4 5 6 7
Mea
n ab
solu
te e
rror
(dB
)
2
1 CS Z
F ull Threshold
esteeded Zestontinued Zest
N ew
S ITA
Mean number of presentations
General height -3dB
4 5 6 7
Mea
n ab
solu
te e
rror
(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