Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis
arXiv:2602.15909v1 Announce Type: cross Abstract: Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class imbalance. To bridge these gaps, we present Resp-Agent, an autonomous multimodal system orchestrated by a novel Active Adversarial Curriculum Agent (Thinker-A$^2$CA). Unlike static pipelines, Thinker-A$^2$CA serves as a central controller that actively identifies diagnostic weaknesses and schedules targeted synthesis in a closed loop. To address the representation gap, we introduce a Modality-Weaving Diagnoser that weaves EHR data with audio tokens via Strategic Global Attention and sparse audio anchors, capturing both long-range clinical context and millisecond-level transients. To address the data gap, we design a Flow Matching Generator that adapts a t
arXiv:2602.15909v1 Announce Type: cross Abstract: Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class imbalance. To bridge these gaps, we present Resp-Agent, an autonomous multimodal system orchestrated by a novel Active Adversarial Curriculum Agent (Thinker-A$^2$CA). Unlike static pipelines, Thinker-A$^2$CA serves as a central controller that actively identifies diagnostic weaknesses and schedules targeted synthesis in a closed loop. To address the representation gap, we introduce a Modality-Weaving Diagnoser that weaves EHR data with audio tokens via Strategic Global Attention and sparse audio anchors, capturing both long-range clinical context and millisecond-level transients. To address the data gap, we design a Flow Matching Generator that adapts a text-only Large Language Model (LLM) via modality injection, decoupling pathological content from acoustic style to synthesize hard-to-diagnose samples. As a foundation for these efforts, we introduce Resp-229k, a benchmark corpus of 229k recordings paired with LLM-distilled clinical narratives. Extensive experiments demonstrate that Resp-Agent consistently outperforms prior approaches across diverse evaluation settings, improving diagnostic robustness under data scarcity and long-tailed class imbalance. Our code and data are available at https://github.com/zpforlove/Resp-Agent.
Executive Summary
The article introduces Resp-Agent, an innovative agent-based system designed to enhance respiratory sound generation and disease diagnosis using multimodal data. The system addresses two key challenges in deep learning-based respiratory auscultation: information loss during signal conversion and limited data availability. Resp-Agent employs a novel Active Adversarial Curriculum Agent (Thinker-A$^2$CA) to actively identify diagnostic weaknesses and schedule targeted synthesis. The Modality-Weaving Diagnoser integrates Electronic Health Record (EHR) data with audio tokens, capturing both long-range clinical context and millisecond-level transients. Additionally, the Flow Matching Generator adapts a text-only Large Language Model (LLM) to synthesize hard-to-diagnose samples. The article presents Resp-229k, a benchmark corpus of 229k recordings paired with LLM-distilled clinical narratives, demonstrating significant improvements in diagnostic robustness under data scarcity and class imbalance.
Key Points
- ▸ Introduction of Resp-Agent, an agent-based system for multimodal respiratory sound generation and disease diagnosis.
- ▸ Addressing information loss and limited data availability in respiratory auscultation.
- ▸ Use of Thinker-A$^2$CA for active identification of diagnostic weaknesses and targeted synthesis.
- ▸ Integration of EHR data with audio tokens via Modality-Weaving Diagnoser.
- ▸ Adaptation of a text-only LLM for synthesizing hard-to-diagnose samples via Flow Matching Generator.
- ▸ Presentation of Resp-229k, a benchmark corpus of 229k recordings with LLM-distilled clinical narratives.
Merits
Innovative Approach
Resp-Agent introduces a novel approach to respiratory sound analysis by integrating multimodal data and employing an active learning framework, which significantly enhances diagnostic accuracy and robustness.
Comprehensive Data Integration
The Modality-Weaving Diagnoser effectively captures both long-range clinical context and millisecond-level transients, providing a more comprehensive analysis of respiratory sounds.
Addressing Data Scarcity
The Flow Matching Generator's ability to synthesize hard-to-diagnose samples addresses the critical issue of data scarcity, improving the system's performance under limited data conditions.
Demerits
Complexity
The system's complexity may pose challenges in implementation and scalability, requiring significant computational resources and expertise.
Data Quality
The effectiveness of the system relies heavily on the quality and accuracy of the EHR data and clinical narratives, which may vary across different healthcare settings.
Generalizability
The system's performance may be limited to the specific conditions and diseases represented in the Resp-229k corpus, potentially affecting its generalizability to other respiratory conditions.
Expert Commentary
The article presents a groundbreaking approach to respiratory sound analysis, leveraging multimodal data and advanced AI techniques to address critical challenges in the field. The use of an active learning framework and the integration of EHR data with audio tokens represent significant advancements in diagnostic technology. However, the complexity of the system and the reliance on high-quality data pose potential limitations. The implications for healthcare practice and policy are substantial, with the potential to enhance diagnostic accuracy and reduce healthcare costs. The article's findings highlight the importance of continued research and development in AI-driven healthcare solutions, with a focus on addressing data quality, privacy, and generalizability issues. The introduction of Resp-229k as a benchmark corpus is a valuable contribution, providing a foundation for future research and validation of respiratory sound analysis systems.
Recommendations
- ✓ Further research to simplify and optimize the Resp-Agent system for broader implementation and scalability.
- ✓ Development of robust data protection measures to ensure the privacy and security of EHR data used in the system.
