Automated Diabetic Retinopathy Screening in Out-patient Diabetes Care – Comparison of Two Artificial Intelligence Algorithms: RetCAD and OphtAI

Abstract

Objective The artificial intelligence (AI) can be applied to screening for diabetic retinopathy (DR) from colour fundus photographs. The prerequisite for this is that the AI used can achieve a similar performance in the real world in different 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 summarised 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 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.

Summary Box

Already known:

• Diabetic retinopathy is a common complication of diabetes mellitus, so screening for detection and early treatment of DR is critical.

• The growing number of people with diabetes is placing an increasing burden on the healthcare system.

• The use of artificial intelligence is well-suited for screening, as retinal imaging combined with an AI-based image analysis algorithm allows for screening examinations to be carried out quickly and resource-consciously.

• Many studies prove that AI-supported image assessment is equivalent to ophthalmological assessment. However, there is evidence in the literature that the algorithms perform worse under real-world conditions compared to results obtained under study conditions.

Newly described:

• For this study, the latest versions of two commercially available AI-based algorithms, RetCAD and OphtAI, were tested for the early detection of diabetic retinopathy.

• The hand-held Aurora retinal camera is recommended, as almost all images could be taken in miosis and evaluated with AI support.

• RetCAD achieved a sensitivity of 90% and specificity of 100% for the detection of referable DR under real-world conditions.

• OphtAI achieved a lower sensitivity of 70% and a specificity of 100% for the detection of referable DR. However, all patients with severe and proliferative DR were correctly assigned.

• With the AI-based algorithms presented here, the number of ophthalmological screening examinations for DR could be reduced by 89%. This would help address the growing number of people with diabetes, while also increasing the capacity for patients with referable DR.

Fazitbox

Bereits bekannt:

• Die diabetische Retinopathie ist eine häufige Komplikation bei Diabetes mellitus, daher ist die Vorsorgeuntersuchung zur Erkennung und frühzeitigen Behandlung der DR entscheidend.

• Die steigende Anzahl an Personen mit Diabetes stellt eine immer größere Belastung für das Gesundheitssystem dar.

• Der Einsatz von künstlicher Intelligenz bietet sich für ein Screening an, da mittels Netzhautkamera und einem auf KI basierendem Algorithmus zur Bildanalyse die Vorsorgeuntersuchung schnell und Ressourcen schonend durchgeführt werden kann.

• Viele Studien belegen, dass die KI-gestützte Bildbeurteilung der augenärztlichen ebenbürtig ist. Allerdings gibt es Hinweise in der Literatur, dass die Algorithmen unter realen Bedingungen schlechter abschneiden, im Vergleich zu den unter Studienbedingungen erzielten Ergebnissen.

Neu beschrieben:

• Für diese Studie wurden die aktuellsten Versionen von 2 kommerziell erhältlichen KI-basierten Algorithmen RetCAD und OphtAI für die Früherkennung einer diabetischen Retinopathie getestet.

• Die handgehaltene Netzhautkamera Aurora ist zu empfehlen, da nahezu alle Aufnahmen in Miosis gemacht und KI-gestützt ausgewertet werden konnten.

• RetCAD erreichte unter realen Bedingungen eine Sensitivität von 90% und Spezifität von 100% für die Erkennung einer überweisungsbedürftigen DR.

• Für OphtAI zeigte sich eine geringere Sensitivität von 70% und eine Spezifität von 100% für die Detektion einer überweisungsbedürftigen DR. Es wurden allerdings alle Patienten mit schwerer und proliferativer DR korrekt zugeordnet.

• Mit den hier vorgestellten KI-basierten Algorithmen ließe sich die Zahl an augenärztlichen Vorsorgeuntersuchungen auf DR um 89% reduzieren. Dadurch könnte die steigende Anzahl an Personen mit Diabetes bewältigt und gleichzeitig mehr Kapazität für Patienten mit überweisungsbedürftiger DR geschaffen werden.

Keywords

artifical intelligence - RetCAD - OphtAI - diabetic retinopathy - screening - Aurora

Publication History

Received: 29 January 2025

Accepted: 13 May 2025

Accepted Manuscript online:
23 May 2025

Article published online:
18 September 2025

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