CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection

• URL: http://arxiv.org/abs/2510.01558v1
• Date: Thu, 02 Oct 2025 01:02:04 GMT
• Title: CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection
• Authors: Zhengyang Shen, Xuehao Zhai, Hua Tu, Mayue Shi,
• Abstract summary: Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication.<n>In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative.<n>We propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features.
• Score: 3.2889108396912974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approaches face challenges such as limited accuracy, reliance on large labeled datasets, and more importantly, weak integration with evidence-based clinical diagnostic indicators. We propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features, including right bundle branch block, left anterior fascicular block, and heart rate variability metrics. The framework uses variational autoencoder-learned representations for semantic case retrieval, providing contextual cases to guide clinical reasoning. Evaluation demonstrated high recall performance of 89.80%, with a maximum F1 score of 0.68 for effective identification of positive cases requiring prioritized serological testing. CardioRAG provides an interpretable, clinical evidence-based approach particularly valuable for resource-limited settings, demonstrating a pathway for embedding clinical indicators into trustworthy medical AI systems.

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