Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06541834
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-26
Brief Title:
Predicting the Risk of Diabetic Neurodegenerative Disorders by Artificial Intelligence Tools Based on Retinal Imaging
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Predicting the Risk of Diabetic Neurodegenerative Disorders by Artificial Intelligence Tools Based on Retinal Imaging-DINEURET PNRR-MCNT2-2023-12378367
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 neurodegenerativecognitive 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 878
Furthermore AI based on retinal imaging has never been applied before to predict the onset and worsening of neurodegenerativecognitive 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 neurodegenerativecognitive 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 neurodegenerativecognitive 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
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 neurodegenerativecognitive 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 878
Furthermore AI based on retinal imaging has never been applied before to predict the onset and worsening of neurodegenerativecognitive 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 neurodegenerativecognitive 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 neurodegenerativecognitive 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None