change detection based on an individual patient’s variability
DESCRIPTION
CHANGE DETECTION BASED ON AN INDIVIDUAL PATIENT’S VARIABILITY. Allison McKendrick Department of Optometry and Vision Science University of Melbourne. Andrew Turpin School of Computer Science and Information Technology RMIT University, Melbourne. Balwantray Chauhan - PowerPoint PPT PresentationTRANSCRIPT
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CHANGE DETECTION BASED ON AN INDIVIDUAL
PATIENT’S VARIABILITY Andrew Turpin
School of Computer Science and Information TechnologyRMIT University, Melbourne
Balwantray ChauhanDepartment of Ophthalmology Dalhousie University, Canada
Allison McKendrick Department of Optometry
and Vision ScienceUniversity of Melbourne
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Can Theory Become Practice?• In theory we know how to customise change probability
maps for individualsTurpin & McKendrick, Vis Res 45, Nov 2005
• How well does it work in practice?
• The method relies on measuring FOS curves at baseline in some number of locations (is this clinically viable)?
• Where do we get a longitudinal dataset that has FOS at baseline…Bal!
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Frequency of Seeing (FOS) Curve
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Frequency of Seeing (FOS) Curve
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Frequency of Seeing (FOS) Curve
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Variability and Thresholds
• Flat FOS curve means less certain responses, wider range of outcomes on a perimeter
• Steep FOS curve, more certain, smaller number of outcomes on a perimeter
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What are the outcomes?
28 32 3082% 82%
SeenNot
Seen 67.24%
60% 60%
36.00%
24 2618% 100%
Seen
40% 85%
NotSeen 18.00%
34.00%
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Full Threshold (stair start = 25 dB)
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Method• Given 2 baseline fields and 6 FOS per patient• Compute slope-threshold relationship• Compute individual probability distributions per
location• Event based
– Flag any locations that fall outside that 95% CI of the probability distribution, compare with GCP
• Trend based– Use probability distributions (plus a bit of maths) as
weights in linear regression, compare with PLR– (No time to discuss in this talk)
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Visit 3
Visit 4
Visit 5
GCP IPoC
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012345678
1 2 3 4 5 6 7 8 9
10 15GCP: 4 loc, 3-of-311 8 9IPoC: 2 loc, 2-of-2 7 8
Number of visits to detect progression
GCP onlyIPoC onlyBoth
No
flagg
ed p
er fi
eld
GCP: 4 loc, 2-of-3 4 9 795
4
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Conclusion
• IPoC event based flagging makes good use of FoS– Flags many less points– Agrees with GCP definition of progression
• IPoC still relies on a definition of baseline– Learning effects will hurt, just as for GCP– Does FoS slope change over time?
• IPoC still at the mercy of unreliable thresholding algorithms and/or false responses
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Trend based - PLR
For progression, slope < -1 and p < 0.01 using 3-omitting scheme
Gardiner & Crabb, IOVS 43, 2002
Slope = 0.1818 p = 0.682
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PLR at visit 4
Slope = -1 p = 0.487
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• Black is high probability of true threshold given all previous measured thresholds, FOS and algorithm details
• (Not simple probability distributions from before)
Weighted PLR
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WLR at visit 5
Slope = -1.4783 p < 0.00001
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Summary• WLR flags at least one location in every patient as
progressing (slope < -1, p < 1%) at Visit 4
• Full Threshold is too noisy to establish baseline after 2 visits (shown in our Vision Research paper)
• Could use different criteria (eg at least 2 locations)
• Just need more data, or less noise, otherwise classification subject to arbitrary criteria and errors
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Slope-Threshold RelationshipFlat
Steep
Grey area is 95% CI from population data Henson et al IOVS 2000
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Slope-Threshold RelationshipFlat
Steep
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Slope-Threshold RelationshipFlat
Steep
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FOS measured using a short MOCS at the 6 red locations
Patient Data
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