Stent Deployment Detection Using Radio Frequency‐Based Sensor and Convolutional Neural Networks

Abstract

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-time monitoring of the stent deployment exists. In recent years, radio frequency (RF)-based 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-based 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-score.