InHealth Intelligence Ltd, Winsford, UK.
Thirona B.V., Nijmegen, The Netherlands.
Diabet Med. 2023 Jun;40(6):e15055. doi: 10.1111/dme.15055. Epub 2023 Mar 2.
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.
9817 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 were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard.
For no diabetic retinopathy versus 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 versus 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.
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.
通过在可以治疗的阶段发现糖尿病性视网膜病变，糖尿病眼病筛查计划在减少可避免的视力丧失方面具有巨大价值。人工智能自动化系统可用于糖尿病眼病筛查，但未纳入全国性的英国糖尿病眼病筛查计划。本研究旨在报告一种商用深度学习人工智能软件在大型英国人群中的表现。
将 10000 例连续糖尿病眼病筛查病例的 9817 组匿名图像集提交给人工智能软件。根据国家方案的糖尿病眼病筛查计划手册分级作为参考标准，确定人工智能系统检测糖尿病性视网膜病变的敏感性和特异性。
对于无糖尿病性视网膜病变与任何糖尿病性视网膜病变，人工智能分级系统的敏感性为 69.7%，特异性为 92.2%。对于无或轻度糖尿病性视网膜病变与显著或可转诊的糖尿病性视网膜病变，人工智能系统的性能更优，敏感性为 95.4%，特异性为 92.0%。人工智能分级未漏诊任何显著的糖尿病性视网膜病变。
本文介绍了一种商用深度学习人工智能系统在英国全国性糖尿病眼病筛查计划中识别糖尿病性视网膜病变的性能。使用预定义的设置，人工智能在识别需要筛查计划采取行动的糖尿病性视网膜病变时性能最高。
