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Assessment of the Abdomen I: Inspection and Auscultation

Assessment of the Abdomen I: Inspection and Auscultation

Introduction
The abdominal examination is a cornerstone of clinical medicine, serving as a critical tool in diagnosing various gastrointestinal (GI) diseases. It involves a systematic approach that includes inspection and auscultation, each with distinct yet complementary roles in assessing the abdomen. This article will delve into these two primary methods healthcare professionals use to examine the abdomen.
Inspection of the Abdomen
The first step in any abdominal examination is inspection.

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Related Experiment Video

Updated: Jun 28, 2026

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and

Annalisa Baronetto^1,2, Luisa Graf³, Sarah Fischer^4,5

¹Hahn-Schickard, Freiburg, Germany.

|July 10, 2024

Summary

This summary is machine-generated.

This study developed an AI model to automatically detect bowel sounds (BSs) using wearable sensors. The Efficient-U-Net model accurately identifies BSs in continuous audio, aiding noninvasive gastrointestinal disorder assessment.

Keywords:

bowel sound deep learning event spotting wearable sensors

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Area of Science:

Biomedical Engineering
Gastroenterology
Signal Processing

Background:

Abdominal auscultation, or listening to bowel sounds (BSs), aids digestion analysis.
Automated BS retrieval offers a noninvasive method for assessing gastrointestinal disorders.

Purpose of the Study:

Develop a multiscale spotting model for detecting BSs in continuous audio data.
Utilize a wearable monitoring system for noninvasive BS detection.

Main Methods:

A spotting model based on the Efficient-U-Net (EffUNet) architecture was designed.
Analysis of 10-second audio segments with 25 ms temporal resolution.
136 hours of labeled data from 18 healthy participants and 9 IBD patients, with 11,482 BSs analyzed.

Main Results:

Median F1-score of 0.73 for BS event spotting in both healthy and IBD groups.
EffUNet achieved 0.73 recall and 0.72 precision across various noise conditions.
High precision (>0.83) at BS ratios >0.05; recall dropped below 0.60 for BS durations ≤1.5 seconds.

Conclusions:

The EffUNet spotter demonstrates robustness against background noise and varying BS durations.
EffUNet surpasses previous BS detection models in unmodified, sparse audio data.
The model facilitates noninvasive gastrointestinal assessment through accurate BS retrieval.