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Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic
Jonathan Krause1, Varun Gulshan1, Ehsan Rahimy2
1Google Research, Google Inc., Mountain View, California.
Ophthalmology
|March 18, 2018
Summary
Adjudication significantly reduces errors in diabetic retinopathy (DR) grading. Training an automated algorithm with adjudicated grades substantially improves its performance, matching expert ophthalmologists.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Diabetic retinopathy (DR) grading is crucial for preventing vision loss.
- Automated DR grading algorithms require accurate training data to minimize errors.
Purpose of the Study:
- To quantify grading errors in diabetic retinopathy (DR) using expert adjudication.
- To train an improved automated algorithm for DR grading using adjudicated data.
Main Methods:
- Retrospective analysis of retinal fundus images from DR screening programs.
- Comparison of grading by an algorithm, ophthalmologists, and retinal specialists.
- Use of adjudicated consensus by retinal specialists as the reference standard.
Main Results:
- The algorithm achieved high agreement (kappa 0.84) with expert consensus.
- For moderate or worse DR, the algorithm demonstrated high sensitivity (0.971) and specificity (0.923).
- Algorithm performance improved significantly (AUC from 0.934 to 0.986) after training with adjudicated grades.
Conclusions:
- Expert adjudication effectively reduces DR grading errors.
- A small set of adjudicated grades substantially enhances automated algorithm performance.
- The improved algorithm performs comparably to human experts in DR grading.
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