Fast diagnosis of bowel activities

Huang, Yung-Fu; Song, Insu; Rana, Priyanka; Koh, Guan

doi:10.1109/ijcnn.2017.7966234

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…The same year, Zhou et al mooted the possibility of detecting BS with spectral entropy analysis [ 101 ]. Huang et al used time series Gaussian Hamming distance to identify BS of one type only [ 102 ]. What warrants attention here is the care the team took to simplify the solution, which finally yielded convincing results.…”

Section: Resultsmentioning

confidence: 99%

Automated Bowel Sound Analysis: An Overview

Nowak

Radzikowski

et al. 2021

Sensors

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Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.

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Section: Resultsmentioning

confidence: 99%

Automated Bowel Sound Analysis: An Overview

Nowak

Radzikowski

et al. 2021

Sensors

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“…Audiovisual spatial-audio analysis can be applied to sound localization and imaging [21] . Time-series Gaussian Hamming distance measures can be used to classify normal and abnormal bowel sounds [22] .…”

Section: Publication Biasmentioning

confidence: 99%

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis

Yan,

Zhang,

Zhang

et al. 2024

Chinese Medical Sciences Journal

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Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome (IBS) with a systematic review and meta-analysis. MethodsWe searched MEDLINE, EMBASE, the Cochrane Library, Web of Science, and IEEE Xplore databases until September 2023. Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified. We estimated the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio with a 95% confidence interval (CI), and plotted a summary receiver operating characteristic curve and evaluated the area under the curve. ResultsFour studies were included. The pooled diagnostic sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.94

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“…Support vector machines have also been used for BS identification [10], including a wearable device [11]. Other more recent approaches included spectral entropy analysis, Gaussian Hamming distance analysis [12]. In China, Liu et al applied voice recognition neural networks to BS identification and obtained encouraging results [13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks

Ficek

Radzikowski

Nowak

et al. 2021

Sensors

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Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy >93%. Moreover, we have achieved a very high specificity (>97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.

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Fast diagnosis of bowel activities

Cited by 10 publications

References 35 publications

Automated Bowel Sound Analysis: An Overview

Automated Bowel Sound Analysis: An Overview

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis

Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks

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