International journal of computer assisted radiology and surgery · 2026
Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).
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Ramos-Polo Raúl, Yun Sergi, Herrador Lorena, de Frutos Fernando, Jovells-Vaqué Sílvia, Cosa Andreea Eunice, Espinosa Alejandro, Ricarte Marin Adrian, Herrero Antón de Vez Hugo, Guardia Oriol, Casasnovas Carlos, Enjuanes Cristina, Reventós Puigjaner Jaime, González-Costello Jose, Comín-Colet Josep
Аннотация
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.
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.