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Baronetto A, Graf L, Fischer S, Neurath MF, Amft O. Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study. JMIR AI 2024; 3:e51118. [PMID: 38985504 PMCID: PMC11269970 DOI: 10.2196/51118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 03/29/2024] [Accepted: 04/24/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. OBJECTIVE This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. METHODS We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. RESULTS Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. CONCLUSIONS The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.
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Affiliation(s)
- Annalisa Baronetto
- Hahn-Schickard, Freiburg, Germany
- Intelligent Embedded Systems Lab, University of Freiburg, Freiburg, Germany
| | - Luisa Graf
- Chair of Digital Health, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sarah Fischer
- Medical Clinic 1, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
| | - Markus F Neurath
- Medical Clinic 1, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
| | - Oliver Amft
- Hahn-Schickard, Freiburg, Germany
- Intelligent Embedded Systems Lab, University of Freiburg, Freiburg, Germany
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Haraguchi T, Emoto T. Stimulus-Response Plots as a Novel Bowel-Sound-Based Method for Evaluating Motor Response to Drinking in Healthy People. SENSORS (BASEL, SWITZERLAND) 2024; 24:3054. [PMID: 38793909 PMCID: PMC11125318 DOI: 10.3390/s24103054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
Constipation is a common gastrointestinal disorder that impairs quality of life. Evaluating bowel motility via traditional methods, such as MRI and radiography, is expensive and inconvenient. Bowel sound (BS) analysis has been proposed as an alternative, with BS-time-domain acoustic features (BSTDAFs) being effective for evaluating bowel motility via several food and drink consumption tests. However, the effect of BSTDAFs before drink consumption on those after drink consumption is yet to be investigated. This study used BS-based stimulus-response plots (BSSRPs) to investigate this effect on 20 participants who underwent drinking tests. A strong negative correlation was observed between the number of BSs per minute before carbonated water consumption and the ratio of that before and after carbonated water consumption. However, a similar trend was not observed when the participants drank cold water. These findings suggest that when carbonated water is drunk, bowel motility before ingestion affects motor response to ingestion. This study provides a non-invasive BS-based approach for evaluating motor response to food and drink, offering a new research window for investigators in this field.
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Affiliation(s)
- Takeyuki Haraguchi
- Science and Technology, Graduate School of Sciences and Technology for Innovation, Tokushima University, Tokushima 770-8506, Japan;
| | - Takahiro Emoto
- Division of Science and Technology, Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan
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Janmohammadi M, Nourbakhsh MS, Bahraminasab M, Tayebi L. Enhancing bone tissue engineering with 3D-Printed polycaprolactone scaffolds integrated with tragacanth gum/bioactive glass. Mater Today Bio 2023; 23:100872. [PMID: 38075257 PMCID: PMC10709082 DOI: 10.1016/j.mtbio.2023.100872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/26/2023] [Accepted: 11/15/2023] [Indexed: 02/12/2024] Open
Abstract
Tissue-engineered bone substitutes, characterized by favorable physicochemical, mechanical, and biological properties, present a promising alternative for addressing bone defects. In this study, we employed an innovative 3D host-guest scaffold model, where the host component served as a mechanical support, while the guest component facilitated osteogenic effects. More specifically, we fabricated a triangular porous polycaprolactone framework (host) using advanced 3D printing techniques, and subsequently filled the framework's pores with tragacanth gum-45S5 bioactive glass as the guest component. Comprehensive assessments were conducted to evaluate the physical, mechanical, and biological properties of the designed scaffolds. Remarkably, successful integration of the guest component within the framework was achieved, resulting in enhanced bioactivity and increased strength. Our findings demonstrated that the scaffolds exhibited ion release (Si, Ca, and P), surface apatite formation, and biodegradation. Additionally, in vitro cell culture assays revealed that the scaffolds demonstrated significant improvements in cell viability, proliferation, and attachment. Significantly, the multi-compartment scaffolds exhibited remarkable osteogenic properties, indicated by a substantial increase in the expression of osteopontin, osteocalcin, and matrix deposition. Based on our results, the framework provided robust mechanical support during the new bone formation process, while the guest component matrix created a conducive micro-environment for cellular adhesion, osteogenic functionality, and matrix production. These multi-compartment scaffolds hold great potential as a viable alternative to autografts and offer promising clinical applications for bone defect repair in the future.
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Affiliation(s)
- Mahsa Janmohammadi
- Department of Biomedical Engineering, Faculty of New Sciences and Technologies, Semnan University, Semnan, Iran
| | | | - Marjan Bahraminasab
- Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
- Nervous System Stem Cells Research Center, Semnan University of Medical Sciences, Semnan, 3513138111, Iran
| | - Lobat Tayebi
- Marquette University School of Dentistry, Milwaukee, WI, 53233, USA
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Song X, Wang G, Zhong W, Guo K, Li Z, Liu X, Dong J, Liu Q. Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration. PHOTOACOUSTICS 2023; 33:100558. [PMID: 38021282 PMCID: PMC10658608 DOI: 10.1016/j.pacs.2023.100558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/14/2023] [Accepted: 09/16/2023] [Indexed: 12/01/2023]
Abstract
As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in low-quality image in photoacoustic tomography. Here, a novel model-based sparse reconstruction method for photoacoustic tomography via diffusion model was proposed. A score-based diffusion model is designed for learning the prior information of the data distribution. The learned prior information is utilized as a constraint for the data consistency term of an optimization problem based on the least-square method in the model-based iterative reconstruction, aiming to achieve the optimal solution. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction compared with conventional reconstruction methods and U-Net. In particular, under the extreme sparse projection (e.g., 32 projections), the proposed method achieves an improvement of ∼ 260 % in structural similarity and ∼ 30 % in peak signal-to-noise ratio for in vivo data, compared with the conventional delay-and-sum method. This method has the potential to reduce the acquisition time and cost of photoacoustic tomography, which will further expand the application range.
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Affiliation(s)
| | | | - Wenhua Zhong
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Kangjun Guo
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Zilong Li
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Xuan Liu
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Jiaqing Dong
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Qiegen Liu
- School of Information Engineering, Nanchang University, Nanchang 330031, China
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Sheikh AB, Sobotka PA, Garg I, Dunn JP, Minhas AMK, Shandhi MMH, Molinger J, McDonnell BJ, Fudim M. Blood Pressure Variability in Clinical Practice: Past, Present and the Future. J Am Heart Assoc 2023; 12:e029297. [PMID: 37119077 PMCID: PMC10227216 DOI: 10.1161/jaha.122.029297] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Recent advances in wearable technology through convenient and cuffless systems will enable continuous, noninvasive monitoring of blood pressure (BP), heart rate, and heart rhythm on both longitudinal 24-hour measurement scales and high-frequency beat-to-beat BP variability and synchronous heart rate variability and changes in underlying heart rhythm. Clinically, BP variability is classified into 4 main types on the basis of the duration of monitoring time: very-short-term (beat to beat), short-term (within 24 hours), medium-term (within days), and long-term (over months and years). BP variability is a strong risk factor for cardiovascular diseases, chronic kidney disease, cognitive decline, and mental illness. The diagnostic and therapeutic value of measuring and controlling BP variability may offer critical targets in addition to lowering mean BP in hypertensive populations.
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Affiliation(s)
- Abu Baker Sheikh
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Paul A. Sobotka
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
| | - Ishan Garg
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Jessilyn P. Dunn
- Department of Biomedical EngineeringDuke UniversityDurhamNCUSA
- Department of Biostatistics & BioinformaticsDuke UniversityDurhamNCUSA
| | | | | | | | - Barry J. McDonnell
- Department of Biomedical ResearchCardiff Metropolitan UniversitySchool of Sport and Health SciencesCardiffUnited Kingdom
| | - Marat Fudim
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
- Duke Clinical Research InstituteDurhamNCUSA
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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.
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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
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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.
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Wang T, He M, Shen K, Liu W, Tian C. Learned regularization for image reconstruction in sparse-view photoacoustic tomography. BIOMEDICAL OPTICS EXPRESS 2022; 13:5721-5737. [PMID: 36733736 PMCID: PMC9872879 DOI: 10.1364/boe.469460] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/07/2022] [Accepted: 10/01/2022] [Indexed: 06/18/2023]
Abstract
Constrained data acquisitions, such as sparse view measurements, are sometimes used in photoacoustic computed tomography (PACT) to accelerate data acquisition. However, it is challenging to reconstruct high-quality images under such scenarios. Iterative image reconstruction with regularization is a typical choice to solve this problem but it suffers from image artifacts. In this paper, we present a learned regularization method to suppress image artifacts in model-based iterative reconstruction in sparse view PACT. A lightweight dual-path network is designed to learn regularization features from both the data and the image domains. The network is trained and tested on both simulation and in vivo datasets and compared with other methods such as Tikhonov regularization, total variation regularization, and a U-Net based post-processing approach. Results show that although the learned regularization network possesses a size of only 0.15% of a U-Net, it outperforms other methods and converges after as few as five iterations, which takes less than one-third of the time of conventional methods. Moreover, the proposed reconstruction method incorporates the physical model of photoacoustic imaging and explores structural information from training datasets. The integration of deep learning with a physical model can potentially achieve improved imaging performance in practice.
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Affiliation(s)
- Tong Wang
- School of Physical Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Menghui He
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China
| | - Kang Shen
- School of Engineering Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Wen Liu
- School of Physical Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Chao Tian
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China
- School of Engineering Science, University of Science and Technology of China, Hefei, Anhui 230026, China
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