The use of artificial intelligence to assess diabetic eye disease among the Greenlandic population

• Published in: International Journal of Circumpolar Health
• Main Authors: Trine Jul Larsen, Maria Bråthen Pettersen, Helena Nygaard Jensen, Michael Lynge Pedersen, Henrik Lund-Andersen, Marit Eika Jørgensen, Stine Byberg
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2024
• Subjects: Diabetic retinopathy; artificial intelligence; screening; ultra wide-field; ICDR-scale; Arctic medicine. Tropical medicine; RC955-962; Circumpolar Health; Greenland; greenlandic; International Journal of Circumpolar Health
• Online Access: https://doi.org/10.1080/22423982.2024.2314802; https://doaj.org/article/b2d456a6b50a4addbc9928c6eced1bf5

ABSTRACTBackground: Retina fundus images conducted in Greenland are telemedically assessed for diabetic retinopathy by ophthalmological nurses in Denmark. Applying an AI grading solution, in a Greenlandic setting, could potentially improve the efficiency and cost-effectiveness of DR screening.Method: We developed an AI model using retina fundus photos, performed on persons registered with diabetes in Greenland and Denmark, using Optos® ultra wide-field scanning laser ophthalmoscope, graded according to ICDR.Using the ResNet50 network we compared the model’s ability to distinguish between different images of ICDR severity levels in a confusion matrix.Results: Comparing images with ICDR level 0 to images of ICDR level 4 resulted in an accuracy of 0.9655, AUC of 0.9905, sensitivity and specificity of 96.6%.Comparing ICDR levels 0,1,2 with ICDR levels 3,4, we achieved a performance with an accuracy of 0.8077, an AUC of 0.8728, a sensitivity of 84.6% and a specificity of 78.8%. For the other comparisons, we achieved a modest performance.Conclusion: We developed an AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images. The sensitivity and specificity were too low for our model to be applied directly in a clinical setting, thus optimising the model should be prioritised.