Predicting the Risk of Diabetic Neurodegenerative Disorders by Artificial Intelligence Tools Based on Retinal Imaging
NCT Number: NCT06541834
Status: NOT_YET_RECRUITING
Study Type: OBSERVATIONAL
Conditions: Diabete Mellitus|Cognitive Impairment
Interventions: DEVICE: Retinal imaging
Sponsor: Ospedale San Raffaele
Collaborators: Riccardo Sacconi|Ilaria Zucchiatti
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 250
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-08-30
Completion Date: 2026-08-30

Summary:
Diabetic Retinopathy (DR) is the most frequent complication of diabetes, and its presence and severity are related to the appearance of both micro and macrovascular events.

Risk profiles have been suggested as a major direction for research in diabetes, based on non- invasive retinal imaging evaluations. There has been promising evidence that artificial intelligence (AI) based on fundus photographs can detect clinical metrics and systemic conditions invisible to expert human observers. Notably, deep-learning (DL) convolutional neural networks (CNNs) developed for retinal photographs have been shown superior performance in the detection of DR compared with human assessment.

The relationship between retinal vascular abnormalities and neurovascular complications of diabetes has been reported. The retina is a window to the body that allows a non-invasive observation of microvascular and neural tissues. However, in clinical practice there are no reported phenotypic indicators or reliable examinations to identify type 2 diabetic (T2D) patients with neurodegenerative/cognitive impairment. The presence of cognitive Impairment is a very frequent complication in diabetic patients, reported up to 60% of the diabetics when compared to only 11 % in the non-diabetics (OR of 8.78).

Furthermore, AI based on retinal imaging has never been applied before to predict the onset and worsening of neurodegenerative/cognitive impairment of T2D in a real-world setting.

The aim of this project is to develop trustworthy AI tools for predicting the risk of developing and progressing of neurodegenerative/cognitive diabetic impairment based on retinal images, in T2D population. For the development and validation of these tools, T2D patients will be enrolled from 4 well-established Italian centers.

The proposal of this study is addressed to health care systems, in order to improve their consciousness about diabetic neurodegenerative/cognitive complications and reduce the related economic burden. Since the huge majority of these disorders remain undiagnosed, DINEURET will provide new cost-effective screening strategies to identify these patients.

4 centers will be involved:

* 75 patients will be included in the IRCCS Ospedale San Raffaele, Milan;
* 75 patients will be included in the IRCCS MultiMedica, Milan;
* 50 patients will be included in the Ospedale Della Murgia "Fabio Perinei", Altamura;
* 50 patients will be included in the Azienda Ospedaliero-Universitaria (AOUI) of Cagliari, Cagliari.

Primary Outcome Measures: Accuracy of AI based model, To evaluate the accuracy (sensibility and specificity) of the AI based model in the prediction of a worsening of neurodegenerative/cognitive impairment (defined as \> 2 point decrease at Montreal Cognitive Assessment scale) based on the retinal imaging acquired at the baseline., 30 August 2026
Secondary Outcome Measures: Reliability and reproducibility of the AI based model To characterize clinical phenotypes within T2D based on the risk of developing and worsening of cognitive decline., To assess the reliability and reproducibility of the AI based model on different sub-samples (patients with different degrees of neurocognitive impairment)., 30 August 2026|Clinical phenotypes of T2D patients, To select clinical phenotypes of T2D patients based on retinal imaging that are characterized by higher risk of developing and worsening of cognitive decline., 30 August 2026