{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:59:55Z","timestamp":1780412395040,"version":"3.54.1"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T00:00:00Z","timestamp":1598486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical findings of auscultation cannot be quantified at the arteriovenous fistula examination site during daily dialysis treatment. Consequently, minute changes over time cannot be recorded based only on subjective observations. In this study, we sought to supplement the daily arteriovenous fistula consultation for hemodialysis patients by recording the sounds made by the arteriovenous fistula and evaluating the sounds using deep learning methods to provide an objective index. We sampled arteriovenous fistula auscultation sounds (192 kHz, 24 bits) recorded over 1 min from 20 patients. We also extracted arteriovenous fistula sounds for each heartbeat without environmental sound by using a convolutional neural network (CNN) model, which was made by comparing these sound patterns with 5000 environmental sounds. The extracted single-heartbeat arteriovenous fistula sounds were sent to a spectrogram and scored using a CNN learning model with bidirectional long short-term memory, in which the degree of arteriovenous fistula stenosis was assigned to one of five sound types (i.e., normal, hard, high, intermittent, and whistling). After 100 training epochs, the method exhibited an accuracy rate of 70\u201393%. According to the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC) was 0.75\u20130.92. The analysis of arteriovenous fistula sound using deep learning has the potential to be used as an objective index in daily medical care.<\/jats:p>","DOI":"10.3390\/s20174852","type":"journal-article","created":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T08:05:18Z","timestamp":1598515518000},"page":"4852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2855-9258","authenticated-orcid":false,"given":"Keisuke","family":"Ota","sequence":"first","affiliation":[{"name":"Department of Nephrology, Gamagori City Hospital, Gamagori 443-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yousuke","family":"Nishiura","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Gamagori City Hospital, Gamagori 443-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saki","family":"Ishihara","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Gamagori City Hospital, Gamagori 443-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hihoko","family":"Adachi","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Gamagori City Hospital, Gamagori 443-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takehisa","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Gamagori City Hospital, Gamagori 443-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takayuki","family":"Hamano","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1053\/j.ajkd.2006.02.181","article-title":"Needle Infiltration of Arteriovenous Fistulae in Hemodialysis: Risk Factors and Consequences","volume":"47","author":"Lee","year":"2006","journal-title":"Am. J. Kidney Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.2215\/CJN.03450413","article-title":"Novel Paradigms for Dialysis Vascular Access: Upstream Hemodynamics and Vascular Remodeling in Dialysis Access Stenosis","volume":"8","author":"Remuzzi","year":"2013","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.kint.2015.12.019","article-title":"The molecular mechanisms of hemodialysis vascular access failure","volume":"89","author":"Brahmbhatt","year":"2016","journal-title":"Kidney Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1053\/j.ajkd.2007.09.012","article-title":"Frequency of Swing-Segment Stenosis in Referred Dialysis Patients With Angiographically Documented Lesions","volume":"51","author":"Badero","year":"2008","journal-title":"Am. J. Kidney Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1016\/j.ejvs.2018.02.001","article-title":"Editor\u2019s Choice\u2014Vascular Access: 2018 Clinical Practice Guidelines of the European Society for Vascular Surgery (ESVS)","volume":"55","author":"Schmidli","year":"2018","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vascular Access 2006 Work Group (2006). National Kidney Foundation Vascular Access 2006 Work Group. KDOQI. Clinical practice guidelines for vascular access. Am. J. Kidney Dis., 48, S176\u2013S247.","DOI":"10.1053\/j.ajkd.2006.04.029"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1053\/j.ajkd.2013.08.023","article-title":"Patency Rates of the Arteriovenous Fistula for Hemodialysis: A Systematic Review and Meta-analysis","volume":"63","author":"Oliver","year":"2014","journal-title":"Am. J. Kidney Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.2215\/CJN.00740113","article-title":"Interventional Nephrology: Physical Examination as a Tool for Surveillance for the Hemodialysis Arteriovenous Access","volume":"8","author":"Salman","year":"2013","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_9","first-page":"332","article-title":"New diagnostic method according to the acoustic analysis of the shunt blood vessel noise","volume":"2","author":"Sato","year":"2005","journal-title":"Toin Univ. Yokohama Eng. Jpn. Soc. Dial. Ther. J."},{"key":"ref_10","first-page":"287","article-title":"Analysis of the shunt sound frequency characteristic changes associated with shunt stenosis","volume":"3","author":"Kokorozashi","year":"2010","journal-title":"Jpn. Soc. Dial. Ther. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Todo, A., Kadonaka, T., Yoshioka, M., Ueno, A., Mitani, M., and Katsurao, H. (2012, January 20\u201324). Frequency Analysis of Shunt Sounds in the Arteriovenous Fistula on Hemodialysis Patients. Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced Intelligence Systems, Kobe, Japan.","DOI":"10.1109\/SCIS-ISIS.2012.6505044"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1109\/TBME.2014.2308906","article-title":"Novel Noninvasive Approach for Detecting Arteriovenous Fistula Stenosis","volume":"61","author":"Wang","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s10157-017-1499-1","article-title":"Continuous monitoring of blood pressure by analyzing the blood flow sound of arteriovenous fistula in hemodialysis patients","volume":"22","author":"Kamijo","year":"2017","journal-title":"Clin. Exp. Nephrol."},{"key":"ref_14","unstructured":"Serven, D., and Brummit, C. (2020, August 26). pyGAM: Generalized Additive Models in Python. Available online: https:\/\/zenodo.org\/record\/1476122."},{"key":"ref_15","unstructured":"Iqbal, T., Kong, Q., Plumbley, M.D., and Wang, W. (2018). Stacked Convolutional Neural Networks for General-Purpose Audio Tagging, University of Surrey."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1159\/000187390","article-title":"Radial Arterial Spasm in Uremic Patients Undergoing Construction of Arteriovenous Hemodial\u03b3sis Fistulas: Diagnosis and Prophylaxis with Intravenous Nicardipine","volume":"64","author":"Owada","year":"1993","journal-title":"Nephron"},{"key":"ref_17","first-page":"2299","article-title":"Cannulation should be more than 3 weeks after creation of a radial-cephalic arterio-venous fistula","volume":"4","author":"Keisuke","year":"2019","journal-title":"Clin. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Akatsuka, J., Yamamoto, Y., Sekine, T., Numata, Y., Morikawa, H., Tsutsumi, K., Yanagi, M., Endo, Y., Takeda, H., and Hayashi, T. (2019). Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches. Biomolecules, 9.","DOI":"10.3390\/biom9110673"},{"key":"ref_20","first-page":"1212","article-title":"Importance of Thorough Physical Examination: A Lost Art","volume":"9","author":"Asif","year":"2017","journal-title":"Cureus"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1109\/TBME.2018.2843258","article-title":"Heart Sound Segmentation\u2014An Event Detection Approach Using Deep Recurrent Neural Networks","volume":"65","author":"Messner","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Raza, A., Mehmood, A., Ullah, S., Ahmad, M., Choi, G.S., and On, B.-W. (2019). Heartbeat Sound Signal Classification Using Deep Learning. Sensors, 19.","DOI":"10.3390\/s19214819"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"569","DOI":"10.4009\/jsdt.52.569","article-title":"A new method for estimating blood flow through arteriovenous fistulas and grafts in patients undergoing hemodialysis","volume":"52","author":"Wakisaka","year":"2019","journal-title":"Jpn. Soc. Dial. Ther."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Higashi, D., and Nishijima, K. (2018, January 12\u201315). Classification of Shunt Murmurs for Diagnosis of Arteriovenous Fistula Stenosis. Proceedings of the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA.","DOI":"10.23919\/APSIPA.2018.8659641"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vununu, C., Moon, K.-S., Lee, S.-H., and Kwon, K.-R. (2018). A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals. Sensors, 18.","DOI":"10.3390\/s18082634"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Heo, S.-J., Kim, Y., Yun, S., Lim, S.-S., Kim, J., Nam, C.M., Park, E.-C., Jung, I., and Yoon, J.-H. (2019). Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers\u2019 Health Examination Data. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16020250"},{"key":"ref_30","first-page":"28","article-title":"Guidelines for basic techniques in vascular access intervention therapy (VAIVT)","volume":"3","author":"Matsuura","year":"2018","journal-title":"Interv. Radiol."},{"key":"ref_31","first-page":"39","article-title":"Usefulness of pulsed doppler ultrasonography to manage internal AV shunt","volume":"55","author":"Murakami","year":"2003","journal-title":"Kidney Dial."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4852\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:40Z","timestamp":1760177260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,27]]},"references-count":31,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174852"],"URL":"https:\/\/doi.org\/10.3390\/s20174852","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,27]]}}}