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Real-world evaluation of RetCAD deep-learning system for the detection of referable diabetic retinopathy and
Joshua R Taylor1, Jocelyn Drinkwater2, David Cordeiro Sousa3
1Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia.
Clinical Relevance:
The challenges of establishing retinal screening programs in rural settings may be mitigated by the emergence of deep-learning systems for early disease detection.
Background:
Deep-learning systems have demonstrated promising results in retinal disease detection and may be particularly useful in rural settings where accessibility remains a barrier to equitable service provision. This study aims to evaluate the real-world performance of Thirona RetCAD for the detection of referable diabetic retinopathy and age-related macular degeneration in a rural Australian population.
Methods:
Colour fundus images from participants with known diabetic retinopathy or age-related macular degeneration were randomly selected from ophthalmology clinics in four rural Australian centres. Grading was confirmed retrospectively by two retinal specialists. RetCAD produced a quantitative measure (0-100) for DR and AMD severity. The area under the ROC curve (AUC) was calculated. Sensitivity, specificity, and positive and negative predictive values were calculated at a pre-defined cut-point of ≥50.
Results:
A total of 150 images from 82 participants were included. The mean age (SD) was 64.0 (12.8) years. Seventy-nine (52.7%) eyes had evidence of referable DR, while 54 (36.0%) had evidence of referable AMD. The AUC for referable DR detection was 0.971 (95% CI 0.950-0.936) with a sensitivity of 86.1% (76.8%-92.0%) and a specificity of 91.6% (82.8%-96.1%) at the pre-defined cut-point. Using the Youden Index method, the optimal cut-point was 41.2 (sensitivity 93.7%, specificity 90.1%). The AUC for the detection of referable AMD was 0.880 (0.824-0.936). At the pre-defined cut-point sensitivity was 88.9% (77.8%-94.8%) and specificity was 66.7% (56.8%-75.3%). The optimal cut-point was 52.6 (sensitivity 87.0%, specificity 75.0%).
Conclusion:
RetCAD is comparable with but does not outperform equivalent deep-learning systems for retinal disease detection. RetCAD may be suitable as an automated screening tool in a rural Australian setting.
