1
|
Kim SH, Jeanne E, Shalish W, Yoo J, Rogers JA. Wireless wearable devices for continuous monitoring of body sounds and motions. Clin Transl Med 2024; 14:e1593. [PMID: 38362612 PMCID: PMC10870079 DOI: 10.1002/ctm2.1593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 02/17/2024] Open
Affiliation(s)
- Sun Hong Kim
- Querrey Simpson Institute for BioelectronicsNorthwestern UniversityEvanstonIllinoisUSA
| | - Emily Jeanne
- Neonatal DivisionDepartment of PediatricsMcGill University Health CenterMontrealQuebecCanada
| | - Wissam Shalish
- Neonatal DivisionDepartment of PediatricsMcGill University Health CenterMontrealQuebecCanada
| | - Jae‐Young Yoo
- Department of Semiconductor Convergence EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
| | - John A. Rogers
- Querrey Simpson Institute for BioelectronicsNorthwestern UniversityEvanstonIllinoisUSA
- Department of Biomedical EngineeringNorthwestern UniversityEvanstonIllinoisUSA
- Department of Materials Science and EngineeringNorthwestern UniversityEvanstonIllinoisUSA
- Department of Neurological SurgeryNorthwestern UniversityChicagoIllinoisUSA
| |
Collapse
|
2
|
Yoo JY, Oh S, Shalish W, Maeng WY, Cerier E, Jeanne E, Chung MK, Lv S, Wu Y, Yoo S, Tzavelis A, Trueb J, Park M, Jeong H, Okunzuwa E, Smilkova S, Kim G, Kim J, Chung G, Park Y, Banks A, Xu S, Sant'Anna GM, Weese-Mayer DE, Bharat A, Rogers JA. Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring. Nat Med 2023; 29:3137-3148. [PMID: 37973946 DOI: 10.1038/s41591-023-02637-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 10/06/2023] [Indexed: 11/19/2023]
Abstract
The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.
Collapse
Affiliation(s)
- Jae-Young Yoo
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Seyong Oh
- Division of Electrical Engineering, Hanyang University ERICA, Ansan, Republic of Korea
| | - Wissam Shalish
- Neonatal Division, Department of Pediatrics, McGill University Health Center, Montreal, Quebec, Canada
| | - Woo-Youl Maeng
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Emily Cerier
- Division of Thoracic Surgery, Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Emily Jeanne
- Neonatal Division, Department of Pediatrics, McGill University Health Center, Montreal, Quebec, Canada
| | - Myung-Kun Chung
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Shasha Lv
- Neonatal Division, Department of Pediatrics, McGill University Health Center, Montreal, Quebec, Canada
| | - Yunyun Wu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Seonggwang Yoo
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Andreas Tzavelis
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Jacob Trueb
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Minsu Park
- Department of Polymer Science and Engineering, Dankook University, Yongin, Republic of Korea
| | - Hyoyoung Jeong
- Department of Electrical and Computer Engineering, University of California, Davis, CA, USA
| | - Efe Okunzuwa
- Division of Thoracic Surgery, Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Slobodanka Smilkova
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Gyeongwu Kim
- Adlai E. Stevenson High School, Lincolnshire, IL, USA
| | - Junha Kim
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Gooyoon Chung
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Yoonseok Park
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Anthony Banks
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Shuai Xu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Sibel Health, Niles, IL, USA
| | - Guilherme M Sant'Anna
- Neonatal Division, Department of Pediatrics, McGill University Health Center, Montreal, Quebec, Canada
| | - Debra E Weese-Mayer
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Autonomic Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
- Stanley Manne Children's Research Institute, Chicago, IL, USA
| | - Ankit Bharat
- Division of Thoracic Surgery, Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA.
| |
Collapse
|
3
|
Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Bărboi O, Iov DE, Nichita L, Ciortescu I, Cijevschi Prelipcean C, Ștefănescu G, Mihai C, Drug VL. The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management. Diagnostics (Basel) 2023; 13:3336. [PMID: 37958232 PMCID: PMC10648815 DOI: 10.3390/diagnostics13213336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual's microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.
Collapse
Affiliation(s)
- Radu Alexandru Vulpoi
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Mihaela Luca
- Institute of Computer Science, Romanian Academy-Iasi Branch, 700481 Iasi, Romania; (M.L.); (A.C.)
| | - Adrian Ciobanu
- Institute of Computer Science, Romanian Academy-Iasi Branch, 700481 Iasi, Romania; (M.L.); (A.C.)
| | - Andrei Olteanu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Oana Bărboi
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Diana-Elena Iov
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Loredana Nichita
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Irina Ciortescu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Cristina Cijevschi Prelipcean
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Gabriela Ștefănescu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Cătălina Mihai
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Vasile Liviu Drug
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| |
Collapse
|
4
|
Ziogas I, Leta V, Lamprou C, Trivedi D, Zinzalias P, Staunton J, Odin P, Chaudhuri KR, Charisis V, Hadjidimitriou S, Stouraitis T, Hadjileontiadis LJ. Dynamic Monitoring of Probiotics Effect in Parkinson's Disease Patients via Swarm Decomposition and Bispectral Analysis of Electrogastrograms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082901 DOI: 10.1109/embc40787.2023.10340198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
People with Parkinson's Disease (PwP) experience a significant deterioration of their daily life quality due to non-motor symptoms, with gastrointestinal dysfunctions manifesting as a vanguard of the latter. Electrogastrography (EGG) is a noninvasive diagnostic tool that can potentially provide biomarkers for the monitoring of dynamic gastric alterations that are related to daily lifestyle and treatment regimens. In this work, a robust analysis of EGG dynamics is introduced to evaluate the effect of probiotic treatment on PwP. The proposed framework, namely biSEGG, introduces a Swarm Decomposition-based enhancement of the EGG, combined with Bispectral feature engineering to model the underlying Quadratic Phase Coupling interactions between the gastric activity oscillatory components of EGG. The biSEGG features are benchmarked against the conventional Power Spectrum-based ones and evaluated through machine learning classifiers. The experimental results, when biSEGG was applied on data epochs from 11 PwP (probiotic vs placebo, AUROC: 0.67, Sensitivity/Specificity: 75/58%), indicate the superiority of biSEGG over Power Spectrum-based approaches and justify the efficiency of biSEGG in capturing and explaining intervention- and meal consumption-related alterations of the gastric activity in PwP.Clinical relevance- biSEGG holds potential for dynamic monitoring of gastrointestinal dysfunction and health status of PwP across diverse daily life scenarios.
Collapse
|
5
|
Shi B, Shen L, Huang W, Cai L, Yang S, Zhang Y, Tou J, Lai D. A Nomogram for Predicting Surgical Timing in Neonates with Necrotizing Enterocolitis. J Clin Med 2023; 12:jcm12093062. [PMID: 37176503 PMCID: PMC10179100 DOI: 10.3390/jcm12093062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/02/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
OBJECTIVE To explore the surgical risk variables in patients with necrotizing enterocolitis (NEC) and develop a nomogram model for predicting the surgical intervention timing of NEC. METHODS Infants diagnosed with NEC were enrolled in our study. We gathered information from clinical data, laboratory examinations, and radiological manifestations. Using LASSO (least absolute shrinkage and selection operator) regression analysis and multivariate logistic regression analysis, a clinical prediction model based on the logistic nomogram was developed. The performance of the nomogram model was evaluated using the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). RESULTS A surgical intervention risk nomogram based on hypothermia, absent bowel sounds, WBC > 20 × 109/L or < 5 × 109/L, CRP > 50 mg/L, pneumatosis intestinalis, and ascites was practical, had a moderate predictive value (AUC > 0.8), improved calibration, and enhanced clinical benefit. CONCLUSIONS This simple and reliable clinical prediction nomogram model can help physicians evaluate children with NEC in a fast and effective manner, enabling the early identification and diagnosis of children at risk for surgery. It offers clinical revolutionary value for the development of medical or surgical treatment plans for children with NEC.
Collapse
Affiliation(s)
- Bo Shi
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Leiting Shen
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Wenchang Huang
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Linghao Cai
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Sisi Yang
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Yuanyuan Zhang
- Department of Pulmonology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Jinfa Tou
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Dengming Lai
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
- Binjiang Institute of Zhejiang University, Hangzhou 310053, China
| |
Collapse
|
6
|
Redij R, Kaur A, Muddaloor P, Sethi AK, Aedma K, Rajagopal A, Gopalakrishnan K, Yadav A, Damani DN, Chedid VG, Wang XJ, Aakre CA, Ryu AJ, Arunachalam SP. Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:2302. [PMID: 36850899 PMCID: PMC9967043 DOI: 10.3390/s23042302] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device-the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson's disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.
Collapse
Affiliation(s)
- Renisha Redij
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Avneet Kaur
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pratyusha Muddaloor
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Arshia K. Sethi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Keirthana Aedma
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Ashima Yadav
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi N. Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Victor G. Chedid
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Xiao Jing Wang
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Shivaram P. Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
7
|
Kutsumi Y, Kanegawa N, Zeida M, Matsubara H, Murayama N. Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone. SENSORS (BASEL, SWITZERLAND) 2022; 23:407. [PMID: 36617005 PMCID: PMC9824196 DOI: 10.3390/s23010407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
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.
Collapse
|
8
|
|
9
|
Wang G, Yang Y, Chen S, Fu J, Wu D, Yang A, Ma Y, Feng X. Flexible dual-channel digital auscultation patch with active noise reduction for bowel sound monitoring and application. IEEE J Biomed Health Inform 2022; 26:2951-2962. [PMID: 35171784 DOI: 10.1109/jbhi.2022.3151927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Bowel sounds (BSs) have important clinical value in the auxiliary diagnosis of digestive diseases, but due to the inconvenience of long-term monitoring and too much interference from environmental noise, they have not been well studied. Most of the current electronic stethoscopes are hard and bulky without the function of noise reduction, and their application for long-term wearable monitoring of BS in noisy clinical environments is very limited. In this paper, a flexible dual-channel digital auscultation patch with active noise reduction is designed and developed, which is wireless, wearable, and conformably attached to abdominal skin to record BS more accurately. The ambient noise can be greatly reduced through active noise reduction based on the adaptive filter. At the same time, some nonstationary noises appearing intermittently (e.g., frictional noise) can also be removed from BS by the cross validation of multichannel simultaneous acquisition. Then, two kinds of typical BS signals are taken as examples, and the feature parameters of the BS in the time domain and frequency domain are extracted through the time-frequency analysis algorithm. Furthermore, based on the short-term energy ratio between the four channels of dual patches, the two-dimensional localization of BS on the abdomen mapping plane is realized. Finally, the continuous wearable monitoring of BS for patients with postoperative ileus (POI) in the noisy ward from pre-operation (POD0) to postoperative Day 7 (POD7) was carried out. The obtained change curve of the occurrence frequency of BS provides guidance for doctors to choose a reasonable feeding time for patients after surgery and accelerate their recovery. Therefore, flexible dual-channel digital auscultation patches with active noise reduction will have promising applications in the clinical auxiliary diagnosis of digestive diseases.
Collapse
|
10
|
Ryu S, Kim SC, Won DO, Bang CS, Koh JH, Jeong IC. iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform. Front Physiol 2022; 13:825612. [PMID: 35237180 PMCID: PMC8883036 DOI: 10.3389/fphys.2022.825612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/21/2022] [Indexed: 11/20/2022] Open
Abstract
Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.
Collapse
Affiliation(s)
- Semin Ryu
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - Seung-Chan Kim
- Department of Sport Interaction Science, Sungkyunkwan University, Suwon, South Korea
| | - Dong-Ok Won
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, South Korea
| | - Jeong-Hwan Koh
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - In cheol Jeong
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- *Correspondence: In cheol Jeong
| |
Collapse
|
11
|
Wang N, Testa A, Marshall BJ. Development of a bowel sound detector adapted to demonstrate the effect of food intake. Biomed Eng Online 2022; 21:1. [PMID: 34983542 PMCID: PMC8729116 DOI: 10.1186/s12938-021-00969-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022] Open
Abstract
Objective Bowel sounds (BS) carry useful information about gastrointestinal condition and feeding status. Interest in computerized bowel sound-based analysis has grown recently and techniques have evolved rapidly. An important first step for these analyses is to extract BS segments, whilst neglecting silent periods. The purpose of this study was to develop a convolutional neural network-based BS detector able to detect all types of BS with accurate time stamps, and to investigate the effect of food consumption on some acoustic features of BS with the proposed detector. Results Audio recordings from 40 volunteers were collected and a BS dataset consisting of 6700 manually labelled segments was generated for training and testing the proposed BS detector. The detector attained 91.06% and 90.78% accuracy for the validation dataset and across-subject test dataset, respectively, with a well-balanced sensitivity and specificity. The detection rates evaluated on different BS types were also satisfactory. Four acoustic features were evaluated to investigate the food effect. The total duration and spectral bandwidth of BS showed significant differences before and after food consumption, while no significant difference was observed in mean-crossing rate values. Conclusion We demonstrated that the proposed BS detector is effective in detecting all types of BS, and providing an accurate time stamp for each BS. The characteristics of BS types and the effect on detection accuracy is discussed. The proposed detector could have clinical application for post-operative ileus prognosis, and monitoring of food intake.
Collapse
Affiliation(s)
- Ning Wang
- The Marshall Centre for Infectious Diseases Research and Training, University of Western Australia, Perth, 6009, Australia.
| | - Alison Testa
- Noisy Guts Pty Ltd, Level 2, L-block, QEII Medical Site, Nedlands, WA, 6009, Australia
| | - Barry J Marshall
- The Marshall Centre for Infectious Diseases Research and Training, University of Western Australia, Perth, 6009, Australia
| |
Collapse
|
12
|
Ficek J, Radzikowski K, Nowak JK, Yoshie O, Walkowiak J, Nowak R. Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks. SENSORS (BASEL, SWITZERLAND) 2021; 21:7602. [PMID: 34833679 PMCID: PMC8618847 DOI: 10.3390/s21227602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/09/2021] [Accepted: 11/14/2021] [Indexed: 11/16/2022]
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.
Collapse
Affiliation(s)
- Jakub Ficek
- Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.F.); (K.R.)
| | - Kacper Radzikowski
- Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.F.); (K.R.)
- Graduate School of Information, Production and Systems, Waseda University, Tokyo 169-8050, Japan;
| | - Jan Krzysztof Nowak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland; (J.K.N.); (J.W.)
| | - Osamu Yoshie
- Graduate School of Information, Production and Systems, Waseda University, Tokyo 169-8050, Japan;
| | - Jaroslaw Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland; (J.K.N.); (J.W.)
| | - Robert Nowak
- Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.F.); (K.R.)
| |
Collapse
|
13
|
Bilionis I, Apostolidis G, Charisis V, Liatsos C, Hadjileontiadis L. Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:915-919. [PMID: 34891439 DOI: 10.1109/embc46164.2021.9630783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gastrointestinal (GI) diseases are amongst the most painful and dangerous clinical cases, due to inefficient recognition of symptoms and thus, lack of early-diagnostic tools. The analysis of bowel sounds (BS) has been fundamental for GI diseases, however their long-term recordings require technical and clinical resources along with the patientt's motionless concurrence throughout the auscultation procedure. In this study, an end-to-end non-invasive solution is proposed to detect BS in real-life settings utilizing a smart-belt apparatus along with advanced signal processing and deep neural network algorithms. Thus, high rate of BS identification and separation from other domestic and urban sounds have been achieved over the realization of an experiment where BS recordings were collected and analyzed out of 10 student volunteers.
Collapse
|
14
|
Nowak JK, Nowak R, Radzikowski K, Grulkowski I, Walkowiak J. Automated Bowel Sound Analysis: An Overview. SENSORS (BASEL, SWITZERLAND) 2021; 21:5294. [PMID: 34450735 PMCID: PMC8400220 DOI: 10.3390/s21165294] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 11/24/2022]
Abstract
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.
Collapse
Affiliation(s)
- Jan Krzysztof Nowak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland;
| | - Robert Nowak
- Artificial Intelligence Division, Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (R.N.); (K.R.)
| | - Kacper Radzikowski
- Artificial Intelligence Division, Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (R.N.); (K.R.)
| | - Ireneusz Grulkowski
- Faculty of Physics, Astronomy and Informatics, Institute of Physics, Nicolaus Copernicus University, 87-100 Toruń, Poland;
| | - Jaroslaw Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland;
| |
Collapse
|
15
|
Deane AM, Ali Abdelhamid Y, Plummer MP, Fetterplace K, Moore C, Reintam Blaser A. Are Classic Bedside Exam Findings Required to Initiate Enteral Nutrition in Critically Ill Patients: Emphasis on Bowel Sounds and Abdominal Distension. Nutr Clin Pract 2020; 36:67-75. [PMID: 33296117 DOI: 10.1002/ncp.10610] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023] Open
Abstract
The general physical examination of a patient is an axiom of critical care medicine, but evidence to support this practice remains sparse. Given the lack of evidence for a comprehensive physical examination of the entire patient on admission to the intensive care unit, which most clinicians consider an essential part of care, should clinicians continue the practice of a specialized gastrointestinal system physical examination when commencing enteral nutrition in critically ill patients? In this review of literature related to gastrointestinal system examination in critically ill patients, the focus is on gastrointestinal sounds and abdominal distension. There is a summary of what these physical features represent, an evaluation of the evidence regarding use of these physical features in patients after abdominal surgery, exploration of the rationale for and against using the physical findings in routine practice, and detail regarding what is known about each feature in critically ill patients. Based on the available evidence, it is recommended that an isolated symptom, sign, or bedside test does not provide meaningful information. However, it is submitted that a comprehensive physical assessment of the gastrointestinal system still has a role when initiating or administering enteral nutrition: specifically, when multiple features are present, clinicians should consider further investigation or intervention.
Collapse
Affiliation(s)
- Adam M Deane
- Intensive Care Unit, Royal Melbourne Hospital, Parkville, Victoria, Australia.,Melbourne Medical School, Department of Medicine and Radiology, Royal Melbourne Hospital, Parkville, The University of Melbourne, Parkville, Victoria, Australia
| | - Yasmine Ali Abdelhamid
- Intensive Care Unit, Royal Melbourne Hospital, Parkville, Victoria, Australia.,Melbourne Medical School, Department of Medicine and Radiology, Royal Melbourne Hospital, Parkville, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark P Plummer
- Intensive Care Unit, Royal Melbourne Hospital, Parkville, Victoria, Australia.,Melbourne Medical School, Department of Medicine and Radiology, Royal Melbourne Hospital, Parkville, The University of Melbourne, Parkville, Victoria, Australia
| | - Kate Fetterplace
- Melbourne Medical School, Department of Medicine and Radiology, Royal Melbourne Hospital, Parkville, The University of Melbourne, Parkville, Victoria, Australia.,Allied Health (Clinical Nutrition), Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Cara Moore
- Intensive Care Unit, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Annika Reintam Blaser
- Department of Anaesthesiology and Intensive Care, University of Tartu, Tartu, Estonia.,Department of Intensive Care, Lucerne Cantonal Hospital, Lucerne, Switzerland
| |
Collapse
|
16
|
Zhao K, Jiang H, Wang Z, Chen P, Zhu B, Duan X. Long-Term Bowel Sound Monitoring and Segmentation by Wearable Devices and Convolutional Neural Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:985-996. [PMID: 32833642 DOI: 10.1109/tbcas.2020.3018711] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Bowel sounds (BSs), typically generated by the intestinal peristalses, are a significant physiological indicator of the digestive system's health condition. In this study, a wearable BS monitoring system is presented for long-term BS monitoring. The system features a wearable BS sensor that can record BSs for days long and transmit them wirelessly in real-time. With the system, a total of 20 subjects' BS data under the hospital environment were collected. Each subject is recorded for 24 hours. Through manual screening and annotation, from every subject's BS data, 400 segments were extracted, in which half are BS event-contained segments. Thus, a BS dataset that contains 20 × 400 sound segments is formed. Afterwards, CNNs are introduced for BS segment recognition. Specifically, this study proposes a novel CNN design method that makes it possible to transfer the popular CNN modules in image recognition into the BS segmentation domain. Experimental results show that in holdout evaluation with corrected labels, the designed CNN model achieves a moderate accuracy of 91.8% and the highest sensitivity of 97.0% compared with the similar works. In cross validation with noisy labels, the designed CNN delivers the best generability. By using a CNN visualizing technique-class activation maps, it is found that the designed CNN has learned the effective features of BS events. Finally, the proposed CNN design method is scalable to different sizes of datasets.
Collapse
|
17
|
Abstract
Irritable bowel syndrome (IBS) is a common and debilitating disorder estimated to affect approximately 11% of the world's population. Typically, IBS is a diagnosis of exclusion after patients undergo a costly and invasive colonoscopy to exclude organic disease. Clinician's and researchers have identified a need for a new cost-effective, accurate, and noninvasive diagnostic test for IBS.
Collapse
|
18
|
Du X, Allwood G, Webberley KM, Osseiran A, Marshall BJ. Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4240. [PMID: 30513934 PMCID: PMC6308494 DOI: 10.3390/s18124240] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 11/22/2018] [Accepted: 11/29/2018] [Indexed: 12/16/2022]
Abstract
Interpretation of bowel sounds (BS) provides a convenient and non-invasive technique to aid in the diagnosis of gastrointestinal (GI) conditions. However, the approach's potential is limited by variation between BS and their irregular occurrence. A short, manual auscultation is sufficient to aid in diagnosis of only a few conditions. A longer recording has the potential to unlock additional understanding of GI physiology and clinical utility. In this paper, a low-cost and straightforward piezoelectric acoustic sensing device was designed and used for long BS recordings. The migrating motor complex (MMC) cycle was detected using this device and the sound index as the biomarker for MMC phases. This cycle of recurring motility is typically measured using expensive and invasive equipment. We also used our recordings to develop an improved categorization system for BS. Five different types of BS were extracted: the single burst, multiple bursts, continuous random sound, harmonic sound, and their combination. Their acoustic characteristics and distribution are described. The quantities of different BS during two-hour recordings varied considerably from person to person, while the proportions of different types were consistent. The sensing devices provide a useful tool for MMC detection and study of GI physiology and function.
Collapse
Affiliation(s)
- Xuhao Du
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| | - Gary Allwood
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| | - Katherine Mary Webberley
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| | - Adam Osseiran
- School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia.
| | - Barry J Marshall
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| |
Collapse
|