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Background <p>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.</p> Methods <p>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).</p> Results <p>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.</p> Conclusions <p>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.</p> Graphical Abstract <p></p>

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Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Raúl Ramos-Polo,
  • Sergi Yun,
  • Lorena Herrador,
  • Fernando de Frutos,
  • Sílvia Jovells-Vaqué,
  • Andreea Eunice Cosa,
  • Alejandro Espinosa,
  • Adrian Ricarte Marin,
  • Hugo Herrero Antón de Vez,
  • Oriol Guardia,
  • Carlos Casasnovas,
  • Cristina Enjuanes,
  • Jaime Reventós Puigjaner,
  • Jose González-Costello,
  • Josep Comín-Colet

摘要

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.

Graphical Abstract