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

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!

Frequency of Seeing (FOS) Curve

Frequency of Seeing (FOS) Curve

Frequency of Seeing (FOS) Curve

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

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%

Full Threshold (stair start = 25 dB)

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)

Visit 3

Visit 4

Visit 5

GCP IPoC

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

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

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

PLR at visit 4

Slope = -1 p = 0.487

• Black is high probability of true threshold given all previous measured thresholds, FOS and algorithm details

• (Not simple probability distributions from before)

Weighted PLR

WLR at visit 5

Slope = -1.4783 p < 0.00001

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

Slope-Threshold RelationshipFlat

Steep

Grey area is 95% CI from population data Henson et al IOVS 2000

Slope-Threshold RelationshipFlat

Steep

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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