Optomed Aurorallla tehty vertailututkimus RetCAD ja OphtAI -algoritmeilla, n=150. Hyvin näyttää Aurora toimivan, ainakin (Icaren/Revenion nykyään omistaman) Retcadin kanssa.
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Automated diabetic retinopathy screening in out-patient diabetes care - comparison of two artificial intelligence algorithms RetCAD and OphtAI
Objective: The application of artificial intelligence (AI) is suitable for diabetic retinopathy (DR) screening from colour fundus photographs. The prerequisite for this is that the AI used can achieve a similar performance in real-world compared to study conditions. The aim of this study is therefore to test and compare the latest version of the AI-based algorithms RetCAD and OphtAI for DR screening in a diabetes outpatient clinic.
Methods: In the period from August 2023 to November 2023, 150 diabetics were recruited at the outpatient diabetes center of the University Hospital. For each study participant, images were taken with the handheld retinal camera Aurora (Optomed Plc, Oulu, Finland) in Miosis. The images were examined by the ophthalmologist and by the AI-based algorithms RetCAD version 2.2.0 (Thirona Retina, Nijmegen, Netherlands) and OphtAI version 2.3.4 (Groupe Evolucare Technologies, Le Pecq, France) for the presence of DR. The severity of DR was classified using the International Clinical Diabetic Retinopathy (ICDR) scale. Patients with no retinal changes or a mild DR were advised to have an ophthalmological check-up in one year. In the presence of a moderate, severe or proliferative DR, a referral to the treating ophthalmologist was made. For this reason, the severity levels of moderate, severe and proliferative DR have been summarized under the umbrella term of referable DR.
Results: No DR was detected in 123 out of 143 (86.0%) diabetics and mild DR was detected in 10 (7.3%). All patients with moderate DR 7 (5.0%), severe 2 (1.5%) and proliferative DR 1 (0.7%) were grouped together as refererable DR and represented a proportion of 7.3%. The AI-based algorithm RetCAD version 2.2.0 achieved a sensitivity of 90% and a specificity of 100% for the detection of a referable DR compared to ophthalmological image assessment. RetCAD rated 98% of the images for image analysis as sufficient or better. In contrast, the second AI-based algorithm OphtAI version 2.3.4 achieved a sensitivity of 70% and a specificity of 100% for the detection of a referable DR. The OphtAI software was able to perform image analysis on all images.
Conclusion: The results for the detection of a referable DR were consistent under study conditions and in clinical use for the AI-based algorithm RetCAD. The AI-based algorithm OphtAI, on the other hand, detected fewer patients with moderate DR, which was reflected in lower a sensitivity. Both algorithms correctly assigned all patients with severe and proliferative DR. The AI-based algorithms RetCAD and OphtAI tested appear to be suitable for use in a diabetes outpatient clinic and primary care setting, respectively.
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