ArticleDiabetology & metabolic syndrome2025
Artificial intelligence algorithm for predicting cardio-cerebrovascular risk in type 2 diabetes: concordance with clinical and instrumental assessments.
Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Harnessing Clinical and Biochemical Data for Personalized Cardiovascular Risk Prediction: a Machine Learning Approach Toward Precision Nutrition.The Journal of nutrition · 2026Observational
- Telehealth for Integrated Cardiovascular and Diabetes Management: A Scoping Review.Journal of diabetes research · 2025Article
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Authors and funding
5 authors.
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Abstract
backgroundThis study aimed to evaluate the predictive performance of an artificial intelligence (AI)-based algorithm in estimating the risk of cardio-cerebrovascular complications in patients with type 2 diabetes mellitus (T2D).
methodsMedical records of 532 T2D patients from the Diabetology Unit in Padova, Italy, were analyzed using the Metaclinic AI Prediction Module, which estimates the probability of heart and cerebrovascular organ damage. For patients identified as "Very high" (n = 63) or "Low" (n = 122) risk for heart disease, additional clinical and instrumental data on their cardiac history were collected. The level of agreement between AI predictions and traditional clinical-instrumental diagnostics was assessed using Cohen's κ coefficient.
resultsIn the "Very high" risk group, the agreement between AI predictions and clinical diagnostics for heart disease was poor (κ = 0.00), while prediction for cerebrovascular disease showed excellent agreement (κ = 0.89). Similarly, in the "Low" risk group, agreement for heart disease remained poor (κ = 0.00), but agreement for cerebrovascular disease was again high (κ = 0.83).
conclusionsA marked difference was observed in the algorithm's performance. While the AI showed strong predictive ability for cerebrovascular complications, it failed to reliably predict heart disease risk. These results suggest that the algorithm may be clinically valuable for cerebrovascular risk assessment but needs refinement for cardiac prediction.
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