Background/aims: Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease / no disease grading can be performed using only microaneurysm detection and image quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable / referable diabetic retinopathy.
Methods: Images from 1253 patients with observable / referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference graded and automated disease / no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection.
Results: Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable / referable retinopathy from 94.9% (95% confidence interval, 93.5-96.0) to 96.6% (95.4-97.4) without affecting manual grading workload.
Conclusion: Automated detection of exudates and haemorrhages improved the detection of observable / referable retinopathy.
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