{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:15:09Z","timestamp":1759968909928,"version":"build-2065373602"},"reference-count":19,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["2018YFB1307703"],"award-info":[{"award-number":["2018YFB1307703"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:sec><jats:label\/><jats:p>A lack of sensory feedback often hinders minimally invasive operations. Although endoscopy has addressed this limitation to an extent, endovascular procedures such as angioplasty or stenting still face significant challenges. Sensors that rely on a clear line of sight cannot be used because it is unable to gather feedback in blood environments. During the stent deployment procedure, feedback on the deployed stent's state is critical because a partially open stent can affect the blood flow. Despite this, no robust and noninvasive clinical solutions that allow real\u2010time monitoring of the stent deployment exists. In recent years, radio frequency (RF)\u2010based sensors can detect the shape and material of an object that is hidden from the direct line of sight. Herein, the use of a 3D RF\u2010based imaging sensor and a novel Convolutional Neural Network (CNN) called StentNet is proposed for detecting the stent's state without a need for a clear line of sight. The StentNet achieves an overall accuracy of 90% in detecting the state of an occluded stent in the test dataset. Compared with an existing CNN model, the StentNet significantly outperforms the 3D LeNet in the evaluation metrics such as accuracy, precision, recall, and F1\u2010score.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202000092","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T07:44:04Z","timestamp":1595231044000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Stent Deployment Detection Using Radio Frequency\u2010Based Sensor and Convolutional Neural Networks"],"prefix":"10.1002","volume":"2","author":[{"given":"Mengya","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering National University of Singapore  Block E3, #03\u201004, 2 Engineering Drive 3 117581 Singapore"},{"name":"National University of Singapore (Suzhou) Research Institute (NUSRI)  No. 377 Linquan Street, Suzhou Industrial Park Suzhou 215123 Jiangsu P. R. 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