Abstract：
OBJECTIVE: Periodic screening for diabetic retinopathy (DR) is effective for preventing blindness. Artificial intelligence (AI) systems could be useful for increasing the screening of DR in diabetic patients. The aim of this study was to compare the performance of the DAIRET system in detecting DR to that of ophthalmologists in a real-world setting.
METHODS: Fundus photography was performed with a nonmydriatic camera in 958 consecutive patients older than 18 years who were affected by diabetes and who were enrolled in the DR screening in the Diabetes and Endocrinology Unit and in the Eye Unit of ULSS8 Berica (Italy) between June 2022 and June 2023. All retinal images were evaluated by DAIRET, which is a machine learning algorithm based on AI. In addition, all the images obtained were analysed by an ophthalmologist who graded the images. The results obtained by DAIRET were compared with those obtained by the ophthalmologist.
RESULTS: We included 958 patients, but only 867 (90.5%) patients had retinal images sufficient for evaluation by a human grader. The sensitivity for detecting cases of moderate DR and above was 1 (100%), and the sensitivity for detecting cases of mild DR was 0.84 ± 0.03. The specificity of detecting the absence of DR was lower (0.59 ± 0.04) because of the high number of false-positives.
CONCLUSIONS: DAIRET showed an optimal sensitivity in detecting all cases of referable DR (moderate DR or above) compared with that of a human grader. On the other hand, the specificity of DAIRET was low because of the high number of false-positives, which limits its cost-effectiveness.
METHODS: Fundus photography was performed with a nonmydriatic camera in 958 consecutive patients older than 18 years who were affected by diabetes and who were enrolled in the DR screening in the Diabetes and Endocrinology Unit and in the Eye Unit of ULSS8 Berica (Italy) between June 2022 and June 2023. All retinal images were evaluated by DAIRET, which is a machine learning algorithm based on AI. In addition, all the images obtained were analysed by an ophthalmologist who graded the images. The results obtained by DAIRET were compared with those obtained by the ophthalmologist.
RESULTS: We included 958 patients, but only 867 (90.5%) patients had retinal images sufficient for evaluation by a human grader. The sensitivity for detecting cases of moderate DR and above was 1 (100%), and the sensitivity for detecting cases of mild DR was 0.84 ± 0.03. The specificity of detecting the absence of DR was lower (0.59 ± 0.04) because of the high number of false-positives.
CONCLUSIONS: DAIRET showed an optimal sensitivity in detecting all cases of referable DR (moderate DR or above) compared with that of a human grader. On the other hand, the specificity of DAIRET was low because of the high number of false-positives, which limits its cost-effectiveness.
摘要：
目的：定期筛查糖尿病性视网膜病变（DR）可有效预防失明。人工智能（AI）系统可用于增加糖尿病患者的DR筛查。这项研究的目的是比较DAIRET系统在检测DR方面的性能与现实环境中眼科医生的性能。
方法：在2022年6月至2023年6月期间，对958名受糖尿病影响的18岁以上的连续患者进行眼底照相，这些患者被纳入糖尿病和内分泌科和ULSS8Berica（意大利）眼科部门的DR筛查。所有视网膜图像均通过DAIRET进行评估，这是一种基于AI的机器学习算法。此外,获得的所有图像均由对图像进行分级的眼科医生进行分析。将DAIRET获得的结果与眼科医生获得的结果进行比较。
结果：我们包括958名患者，但只有867例（90.5%）患者的视网膜图像足以由人类分级者进行评估。检测中度DR及以上病例的灵敏度为1(100%)，检测轻度DR病例的灵敏度为0.84±0.03。检测不存在DR的特异性较低（0.59±0.04），因为假阳性的数量很多。
结论：与人类分级者相比，DAIRET在检测所有相关DR（中度DR或以上）病例中显示出最佳灵敏度。另一方面,DAIRET的特异性很低，因为假阳性的数量很多，这限制了它的成本效益。
方法：在2022年6月至2023年6月期间，对958名受糖尿病影响的18岁以上的连续患者进行眼底照相，这些患者被纳入糖尿病和内分泌科和ULSS8Berica（意大利）眼科部门的DR筛查。所有视网膜图像均通过DAIRET进行评估，这是一种基于AI的机器学习算法。此外,获得的所有图像均由对图像进行分级的眼科医生进行分析。将DAIRET获得的结果与眼科医生获得的结果进行比较。
结果：我们包括958名患者，但只有867例（90.5%）患者的视网膜图像足以由人类分级者进行评估。检测中度DR及以上病例的灵敏度为1(100%)，检测轻度DR病例的灵敏度为0.84±0.03。检测不存在DR的特异性较低（0.59±0.04），因为假阳性的数量很多。
结论：与人类分级者相比，DAIRET在检测所有相关DR（中度DR或以上）病例中显示出最佳灵敏度。另一方面,DAIRET的特异性很低，因为假阳性的数量很多，这限制了它的成本效益。
