Towards Objective Gastrointestinal Auscultation: Automated Segmentation and Annotation of Bowel Sound Patterns
Abstract
Bowel sounds (BS) are typically momentary and have low amplitude, making them difficult to detect accurately through manual auscultation. This leads to significant variability in clinical assessment. Digital acoustic sensors allow the acquisition of high-quality BS and enable automated signal analysis, offering the potential to provide clinicians with both objective and quantitative feedback on bowel activity. This study presents an automated pipeline for bowel sound segmentation and classification using a wearable acoustic SonicGuard sensor. BS signals from 83 subjects were recorded using a SonicGuard sensor. Data from 40 subjects were manually annotated by clinical experts and used to train an automatic annotation algorithm, while the remaining subjects were used for further model evaluation. An energy-based event detection algorithm was developed to detect BS events. Detected sound segments were then classified into BS patterns using a pretrained Audio Spectrogram Transformer (AST) model. Model performance was evaluated separately for healthy individuals and patients. The best configuration used two specialized models, one trained on healthy subjects and one on patients, achieving (accuracy: 0.97, AUROC: 0.98) for healthy group and (accuracy: 0.96, AUROC: 0.98) for patient group. The auto-annotation method reduced manual labeling time by approximately 70%, and expert review showed that less than 12% of automatically detected segments required correction. The proposed automated segmentation and classification system enables quantitative assessment of bowel activity, providing clinicians with an objective diagnostic tool that may improve the diagnostic of gastrointestinal function and support the annotation of large-scale datasets.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of an automated pipeline for bowel sound segmentation and classification that integrates advanced machine learning techniques with a wearable acoustic sensor, addressing the challenges of subjective auscultation in clinical practice. The comprehensive methodology and promising results indicate a significant step forward in the objective assessment of gastrointestinal function, with potential implications for clinical diagnostics and research.
Methodology
The paper presents a comprehensive automated pipeline for bowel sound segmentation and classification, utilizing a wearable acoustic sensor. The methodology is well-structured, combining an energy-based event detection algorithm with advanced deep learning models (Audio Spectrogram Transformer and Wav2Vec). The approach is innovative in its integration of cohort-specific models to account for differences between healthy individuals and patients, which is a significant advancement over previous works that did not consider such variability. The detailed description of the event detection algorithm, including the use of RMS amplitude and energy variations, demonstrates a thoughtful approach to addressing the challenges posed by the heterogeneous nature of bowel sounds.
Results & evaluation
The experiments are robust, involving recordings from a diverse set of subjects (both healthy and patients) and a well-defined evaluation protocol. The performance metrics (accuracy and AUROC) indicate strong model performance, particularly with the AST model achieving high accuracy rates (0.97 for healthy subjects and 0.96 for patients). The use of expert-reviewed annotations adds credibility to the evaluation process. However, the paper could benefit from additional comparative analyses with other state-of-the-art methods to further validate the proposed approach.
Reproducibility
The authors provide a GitHub repository for the implementation of their approach, which is a positive aspect for reproducibility. However, the paper lacks detailed information on the specific experimental setup, such as hyperparameter tuning and training procedures, which could hinder full reproducibility by other researchers.
Limitations
The study acknowledges limitations, such as the tendency of the auto-annotation framework to truncate certain event durations, particularly for the MB class. Additionally, the reliance on a relatively small dataset for training and evaluation may affect the generalizability of the model. The authors could also explore the impact of noise and other external factors on the model's performance in real-world clinical settings.
Broader impact
The proposed automated system has significant potential applications in clinical settings, providing objective and quantitative assessments of bowel sounds that could enhance diagnostic accuracy and efficiency. By reducing the workload on clinicians and enabling the analysis of large datasets, this work could facilitate improved patient monitoring and treatment strategies in gastrointestinal care. The development of such tools aligns with the growing trend towards digital health and personalized medicine. The main contribution of this paper is the development of an automated pipeline for bowel sound segmentation and classification that integrates advanced machine learning techniques with a wearable acoustic sensor, addressing the challenges of subjective auscultation in clinical practice. The comprehensive methodology and promising results indicate a significant step forward in the objective assessment of gastrointestinal function, with potential implications for clinical diagnostics and research.
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