Stent Deployment Detection Using Radio Frequency‐Based Sensor and Convolutional Neural Networks
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 un...
Main Authors: | Mengya Xu, Lalithkumar Seenivasan, Leonard Leong Litt Yeo, Hongliang Ren |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-10-01
|
Series: | Advanced Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1002/aisy.202000092 |
Similar Items
-
Computational modeling of balloon-expandable stent deployment in coronary artery using the finite element method
by: Umer M, et al.
Published: (2019-09-01) -
Impact of stent deployment pressure and poststenting dilatation on the outcome of elective percutaneous coronary intervention
by: Nour MK
Published: (2016-09-01) -
Impact of very high pressure stent deployment on angiographic and long-term clinical outcomes in true coronary bifurcation lesions treated by the mini-crush stent technique: A single center experience
by: Antoine Gerbay, et al.
Published: (2017-01-01) -
Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition
by: Ming Zhang, et al.
Published: (2017-01-01) -
Patient-Specific Virtual Stent-Graft Deployment for Type B Aortic Dissection: A Pilot Study of the Impact of Stent-Graft Length
by: Xiaoxin Kan, et al.
Published: (2021-07-01)