In Diabetic medicine : a journal of the British Diabetic Association
AIMS : A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep learning artificial intelligence software in a large English population.
METHODS : 9,817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy was determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard.
RESULTS : For no diabetic retinopathy vs any diabetic retinopathy the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy vs significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy.
CONCLUSION : The performance of a commercially available deep learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre-defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme.
Meredith Sarah, Grinsven Mark van, Engelberts Jonne, Clarke Dominic, Prior Vicki, Vodrey Jo, Hammond Alison, Muhammed Raja, Kirby Philip
2023-Jan-31
