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Aim: 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., Conclusion: 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., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no conflicts of interest. Ethics approval: Approval was obtained from the ethics committee of ULSS8 Berica. The procedures used in this study adhered to the tenets of the Declaration of Helsinki. Informed consent: Informed consent was obtained from all individual participants included in the study., (© 2024. Springer-Verlag Italia S.r.l., part of Springer Nature.)

The prevalence of gestational diabetes in the developed world is increased and parallels that of obesity. Apart from the maternal and fetal complications occurring during pregnancy, GDM is characterized by a high subsequent risk of type 2 diabetes, metabolic syndrome, and cardiovascular disease. In this paper, we outline the different factors to consider in assessing the future risk of diabetes developing in women with a history of GDM. Looking at the modifiable risk factors, it is worth noting that promoting a healthy diet and lifestyle before (physical activity), during and after pregnancy (breast feeding) in women of fertile age are fundamental to the success of efforts to reduce the burden of diabetes in these young people., (Copyright © 2018 Elsevier B.V. All rights reserved.)