The MAESTRIA Demonstrator significantly improved cardiovascular risk prediction accuracy by up to 0.15 AUC over standard clinical scores using integrated multimodal data.
Does the AI-driven multimodal MAESTRIA Demonstrator improve prediction accuracy for atrial fibrillation recurrence, stroke risk, and atrial cardiomyopathy compared to standard clinical scores?
An AI-driven multimodal clinical decision support tool integrating clinical, imaging, and omics data improves the prediction of atrial fibrillation and stroke risk compared to standard clinical scores.
Tasa de eventos absoluta: 0% vs 0%
Abstract Background Personalised cardiovascular risk prediction remains limited by fragmented data and lack of clinical interpretability. The MAESTRIA project aims to overcome these limitations by developing an AI-powered, multimodal Clinical Decision Support Demonstrator for stroke and atrial fibrillation (AF) risk stratification. Purpose To develop and validate an interoperable demonstrator integrating imaging, electrophysiology, clinical and omics data using explainable AI to support personalised cardiovascular care. Methods We harmonised multimodal datasets across several European centers, including clinical variables, ECG, echocardiography, cardiac-MRI, CT, wearable-datasets and omics. Predictive models were developed using ensemble machine learning algorithms (e.g., RandomForest, Logistic Regression, CatBoost, XGBoost), deep learning approaches for imaging segmentation, and multi-block data integration frameworks. Model interpretability was addressed through SHAP value analysis and partial dependence plots. The demonstrator was built as a GDPR-compliant, decision support tool with clinician-friendly interfaces. Results The demonstrator integrates validated models predicting AF recurrence, stroke risk, and atrial cardiomyopathy. By integrating ECG, echo, MRI, CT, and omics data, it significantly outperforms standard clinical scores, boosting prediction accuracy (AUC increase up to 0.15). External validation across multiple cohorts confirmed generalisability and clinical usability. Conclusion The MAESTRIA Demonstrator, built on a robust multimodal AI framework, promises to be a powerful clinical tool enabling clinicians to predict, visualize, and interpret cardiovascular risk, particularly atrial fibrillation, through the integration of clinical, imaging, and biological data.
Ponnaiah et al. (Thu,) reported a other. The MAESTRIA Demonstrator significantly improved cardiovascular risk prediction accuracy by up to 0.15 AUC over standard clinical scores using integrated multimodal data.