Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks

Abstract

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.

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

“…The studies between 1967 and 2022 on this data set have been summarized in Table 1 [ 21 , 22 , 28 , 42 , 61 , 65 , 83 , 87 , 88 , 89 , 91 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ].…”

Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone

“…BSs can be analyzed directly by a physician or using a learning model that recognizes sounds collected using a special recording instrument [ 4 , 6 , 8 , 9 , 10 , 11 ]. For instance, Craine et al [ 4 ] used a method involving stationary-nonstationary filters with wavelet transformation, whereas Wang et al [ 7 ] used an artificial neural network and Sakata and Suzuki [ 11 ] used a classification algorithm capturing sound features.…”

“…In this study, we constructed a BS recognition model based on sounds obtained by subjects themselves using a smartphone and examined whether the model had similar accuracy to previous models based on BSs obtained using dedicated devices. Since general-purpose recording devices can result in strong noise, we used a convolutional neural network (CNN) that can automatically extract features from input data with high accuracy and low noise, which has previously been used for image, speech, and more recently, BS recognition [ 8 , 9 ]. The CNN model constructed using built-in smartphone microphones could recognize BS with moderate accuracy.…”

“…The following supporting information can be downloaded at: , Table S1: Parameter tuning results of the convolutional layers and the number of channels, Table S2: Parameter tuning results of the kernel size, Table S3: Model performance between the recent similar work by Ficek et al [ 9 ] and ours with the F measure.…”

Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates

“…The new continuous acoustic recorder employs Bluetooth transmission technology to gather data and a deep neural network algorithm model to achieve better noise reduction, lower power consumption, higher recognition rate, and continuous monitoring ( 4 , 5 ). It can provide the sound waveform and spectrum of bowel sounds to accurately and impartially reflect intestinal motility ( 6 ). The approach has been used in adult medicine and surgery to monitor the recovery of bowel function after surgery, diagnose acute peritonitis, identify early intestinal obstruction, and diagnose diarrheal disease ( 7 , 8 ).…”