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September 5, 2025Diabetology & Metabolic SyndromeOpen Access

Artificial intelligence algorithm for predicting cardio-cerebrovascular risk in type 2 diabetes: concordance with clinical and instrumental assessments

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Authors

FPFrancesco PiarulliUniversity of Padua EREugenio RagazziUniversity of Padua CCChiara Celeste Celsan

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Implication

Observational analysis evaluates AI's predictive performance for cardiovascular issues in T2D patients, indicating strong cerebrovascular risk assessment potential.

Key Points

AI demonstrated strong predictive ability for cerebrovascular complications in type 2 diabetes patients, while performing poorly for heart disease risk.
Cohen's κ coefficient showed poor agreement for heart disease (κ = 0.00) but excellent agreement for cerebrovascular disease (κ = 0.89) in the very high risk group.
Medical records of 532 T2D patients were analyzed using the Metaclinic AI Prediction Module, focusing on predictive performance for cardio-cerebrovascular complications.
The algorithm may be clinically valuable for cerebrovascular risk assessment in T2D, but it requires refinement for more accurate cardiac predictions.

Cite This Study

Piarulli et al. (2025) studied this question.

synapsesocial.com/papers/68bb3a492b87ece8dc9557b5 https://doi.org/10.1186/s13098-025-01910-6

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Implication

Observational analysis evaluates AI's predictive performance for cardiovascular issues in T2D patients, indicating strong cerebrovascular risk assessment potential.

Artificial intelligence algorithm for predicting cardio-cerebrovascular risk in type 2 diabetes: concordance with clinical and instrumental assessments

Discussion

Member takes

Implication

Key Points

Cite This Study

Artificial intelligence algorithm for predicting cardio-cerebrovascular risk in type 2 diabetes: concordance with clinical and instrumental assessments

Discussion

Member takes

Implication

Key Points

Cite This Study

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