{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:47:17Z","timestamp":1726188437156},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["NSTC 112-2221-E-214-013"]},{"name":"I-Shou University, Taiwan","award":["ISU-112-IUC-01"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow sounds before and after angioplasty surgery, analyzing 3819 sound samples from 119 dialysis patients. These sound signals were transformed into spectrogram images to classify obstructed and unobstructed vascular accesses, that is fistula conditions before and after the angioplasty procedure. A novel lightweight two-dimension convolutional neural network (CNN) was developed and benchmarked against pretrained CNN models such as ResNet50 and VGG16. The proposed model achieved a prediction accuracy of 100%, surpassing the ResNet50 and VGG16 models, which recorded 99% and 95% accuracy, respectively. Additionally, the study highlighted the significantly smaller memory size of the proposed model (2.37 MB) compared to ResNet50 (91.3 MB) and VGG16 (57.9 MB), suggesting its suitability for edge computing environments. This study underscores the efficacy of diverse deep-learning approaches in the obstructed detection of dialysis fistulas, presenting a scalable solution that combines high accuracy with reduced computational demands.<\/jats:p>","DOI":"10.3390\/s24185922","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T12:04:09Z","timestamp":1726142649000},"page":"5922","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8619-2060","authenticated-orcid":false,"given":"Jia-Jung","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, I-Shou University, Kaohsiung 82445, Taiwan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4964-6403","authenticated-orcid":false,"given":"Alok Kumar","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3923-4387","authenticated-orcid":false,"given":"Shing-Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan"}]},{"given":"Hangliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, I-Shou University, Kaohsiung 82445, Taiwan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7938-9033","authenticated-orcid":false,"given":"Wenxi","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Information Systems, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City 965-8580, Fukushima, Japan"}]},{"given":"Thung-Lip","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Cardiology, E-Da Hospital, Kaohsiung 84001, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1007\/s10439-005-5367-X","article-title":"Hemodynamics and Complications Encountered with Arteriovenous Fistulas and Grafts as Vascular Access for Hemodialysis: A Review","volume":"33","author":"Tordoir","year":"2005","journal-title":"Ann. 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