ArticleInternational journal of computer assisted radiology and surgery2026
Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).
Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundEarly 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.
methodsUsing 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).
resultsA 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.
conclusionsOur 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.
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Registered trials
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