An AI-driven multidimensional patient-specific model integrating clinical, electrocardiographic, and echocardiographic data achieved an AUC of 0.84 for the detection of transthyretin cardiac amyloidosis.
Observational (n=124)
No
Does an AI-driven multimodal data integration model improve the early detection of ATTR-CM in heart failure patients with suspected cardiac amyloidosis?
An explainable AI model integrating clinical, ECG, and echocardiographic data achieved strong diagnostic performance (AUC 0.84) for the early detection of transthyretin cardiac amyloidosis, significantly outperforming imaging data alone.
Absolute Event Rate: 0.84% vs 0.56%
BACKGROUND: Early diagnosis of transthyretin cardiac amyloidosis (ATTR-CM) is essential for timely intervention but remains challenging due to its subtle and nonspecific clinical presentation. The CRONOS-ATTR study aimed to improve early detection of ATTR-CM by integrating multimodal data (clinical, electrocardiographic, and echocardiographic) within a model-guided medicine framework. METHODS: Using artificial intelligence (AI) algorithms from CardiolyseECGSoftware and Ligence Heart, along with human intelligence (multidimensional interpretable models), we standardized and harmonized heterogeneous data sources into a unified patient-specific model (PSM). RESULTS: A machine learning model based on XGBoost was trained on a cohort of 124 patients and achieved strong diagnostic performance (AUC 0.84), with high sensitivity and precision. The model provided interpretable outputs using SHAP values, facilitating clinical understanding and trust. This approach not only enabled accurate early detection of ATTR-CM but also demonstrated feasibility for integration into real-world clinical workflows. CONCLUSIONS: Our findings support the use of explainable AI to enhance screening strategies for cardiac amyloidosis and establish a foundation for scalable, automated tools that can be embedded within healthcare systems.
Ramos-Polo et al. (2026) conducted an observational in Transthyretin-associated cardiac amyloidosis (ATTR-CM) (n=124). AI-driven multidimensional patient-specific model vs. ECG and echocardiographic data only was evaluated on Diagnostic performance (AUC) for detecting ATTR-CM. An AI-driven multidimensional patient-specific model integrating clinical, electrocardiographic, and echocardiographic data achieved an AUC of 0.84 for the detection of transthyretin cardiac amyloidosis.