{"doi":"10.3390/s23010407","title":"Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone","abstract":"<jats:p>Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.</jats:p>","journal":"Sensors","year":2022,"id":634696,"datarank":0.5456379239589579,"base_score":3.6375861597263857,"endowment":3.6375861597263857,"self_citation_contribution":0.5456379239589579,"citation_network_contribution":0.0,"self_endowment_contribution":0.5456379239589579,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":37,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1646254,"name":"Norimasa Kanegawa","orcid":null,"position":1,"is_corresponding":false},{"id":1646256,"name":"Mitsuhiro Zeida","orcid":null,"position":2,"is_corresponding":false},{"id":1646258,"name":"Hitoshi Matsubara","orcid":null,"position":3,"is_corresponding":false},{"id":1646259,"name":"Norihito Murayama","orcid":null,"position":4,"is_corresponding":false},{"id":1646253,"name":"Yuka Kutsumi","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone","abstract":"<jats:p>Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.</jats:p>","is_dataset_classified":null,"base_score":3.58351893845611,"endowment":3.58351893845611,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36617005","pmcid":"PMC9824196","openalex_id":"https://openalex.org/W4313473044","authors":[],"funders":[],"total_grants":0,"fwci":3.6729,"citation_percentile":0.94765721,"influential_citations":0,"citation_trend":[{"year":2023,"count":9},{"year":2024,"count":7},{"year":2025,"count":11},{"year":2026,"count":8}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/1424-8220/23/1/407/pdf?version=1672391733","host_type":"journal"},{"url":"https://www.mdpi.com/1424-8220/23/1/407/pdf?version=1672391733","host_type":"publisher"},{"url":"https://www.mdpi.com/1424-8220/23/1/407/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/s23010407","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/36617005","host_type":"repository"},{"url":"https://doaj.org/article/3d5094cf8db947f08442bf5d542348b9","host_type":"repository"},{"url":"https://dx.doi.org/10.3390/s23010407","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9824196","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC9824196","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC9824196?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Phonocardiography and Auscultation Techniques","Music and Audio Processing","Music Therapy and Health"],"mesh_terms":["Smartphone","Adult","Algorithms","Humans","Middle Aged","Sound","Mobile Applications"],"keywords":["Computer science","Annotation","Artificial intelligence","Sound analysis","Measure (data warehouse)","Speech recognition","Pattern recognition (psychology)","Data mining","Acoustics","Neural network","Machine Learning","Deep Learning","Bowel Sound"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T14:02:00.469389Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}