PulmoVec: A Two-Stage Stacking Meta-Learning Architecture Built on the HeAR Foundation Model for Multi-Task Classification of Pediatric Respiratory Sounds
Abstract
Background: Respiratory diseases are a leading cause of childhood morbidity and mortality, yet lung auscultation remains subjective and limited by inter-listener variability, particularly in pediatric populations. Existing AI approaches are further constrained by small datasets and single-task designs. We developed PulmoVec, a multi-task framework built on the Health Acoustic Representations (HeAR) foundation model for classification of pediatric respiratory sounds. Methods: In this retrospective analysis of the SPRSound database, 24,808 event-level annotated segments from 1,652 pediatric patients were analyzed. Three task-specific classifiers were trained for screening, sound-pattern recognition, and disease-group prediction. Their out-of-fold probability outputs were combined with demographic metadata in a LightGBM stacking meta-model, and event-level predictions were aggregated to the patient level using ensemble voting. Results: At the event level, the screening model achieved an ROC-AUC of 0.96 (95% CI, 0.95-0.97), the sound-pattern recognition model a macro ROC-AUC of 0.96 (95% CI, 0.96-0.97), and the disease-group prediction model a macro ROC-AUC of 0.94 (95% CI, 0.93-0.94). At the patient level, disease-group classification yielded an accuracy of 0.74 (95% CI, 0.71-0.77), a weighted F1-score of 0.73, and a macro ROC-AUC of 0.91 (95% CI, 0.90-0.93). Stacking improved performance across all tasks compared with base models alone. Conclusions: PulmoVec links event-level acoustic phenotyping with patient-level clinical classification, supporting the potential of foundation-model-based digital auscultation in pediatric respiratory medicine. Multi-center external validation across devices and real-world conditions remains essential.
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Research summary
The paper presents PulmoVec, a novel multi-task framework that leverages a foundation model for pediatric respiratory sound classification, demonstrating significant potential for improving clinical decision support in pediatric respiratory medicine. The integration of demographic data and the innovative stacking approach contribute to its relevance and potential impact in the field.
Methodology
The methodology is robust, employing a two-stage stacking meta-learning architecture that integrates a foundation model (HeAR) with task-specific classifiers. The use of LightGBM for stacking and the incorporation of demographic metadata is innovative, enhancing the model's clinical relevance. The approach to fine-tuning and the ensemble voting strategy for patient-level predictions are well-structured, although the absence of temporal modeling in the aggregation process could be a potential area for improvement.
Results & evaluation
The experiments are thorough, utilizing a substantial dataset (24,808 event-level segments from 1,652 patients) and providing detailed performance metrics across multiple tasks. The reported ROC-AUC values and accuracy scores indicate strong performance, particularly at the event level. However, the transition from event-level to patient-level predictions shows a decrease in accuracy, which is a critical observation that warrants further exploration.
Reproducibility
The authors have made their code and data publicly available, which supports reproducibility. The detailed description of the methods, including data preprocessing, model training, and evaluation metrics, enhances the transparency of the research. However, the reliance on a single-center dataset may limit the generalizability of the findings.
Limitations
Key limitations include the lack of external validation, which raises concerns about the model's performance in diverse clinical settings. The study also does not address the potential impact of annotation noise and the variability of respiratory sounds across different devices and environments. Additionally, the low recall for the Normal class at the patient level highlights a significant challenge in accurately classifying normal respiratory sounds.
Broader impact
The findings have significant implications for pediatric respiratory medicine, particularly in enhancing diagnostic accuracy through AI-assisted auscultation. The potential for digital auscultation to improve clinical decision-making and patient outcomes is substantial, but further validation across diverse populations and settings is essential for real-world applicability. The paper presents PulmoVec, a novel multi-task framework that leverages a foundation model for pediatric respiratory sound classification, demonstrating significant potential for improving clinical decision support in pediatric respiratory medicine. The integration of demographic data and the innovative stacking approach contribute to its relevance and potential impact in the field.
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