Deep Learning for Needle Detection in a Cannulation Simulator

TL;DR

This paper explores deep learning methods to detect needle infiltration in a cannulation simulator using video analysis, demonstrating real-time performance with lightweight neural networks.

Contribution

It introduces a deep learning approach for needle state detection in a cannulation simulator, comparing various neural network architectures and implementing a real-time solution.

Findings

CRNN outperforms baseline models in accuracy

Light CNN and CRNN achieve better results than pre-trained models

Real-time implementation verified on commodity hardware

Abstract

Cannulation for hemodialysis is the act of inserting a needle into a surgically created vascular access (e.g., an arteriovenous fistula) for the purpose of dialysis. The main risk associated with cannulation is infiltration, the puncture of the wall of the vascular access after entry, which can cause medical complications. Simulator-based training allows clinicians to gain cannulation experience without putting patients at risk. In this paper, we propose to use deep-learning-based techniques for detecting, based on video, whether the needle tip is in or has infiltrated the simulated fistula. Three categories of deep neural networks are investigated in this work: modified pre-trained models based on VGG-16 and ResNet-50, light convolutional neural networks (light CNNs), and convolutional recurrent neural networks (CRNNs). CRNNs consist of convolutional layers and a long short-term memory (LSTM) layer. A data set of cannulation experiments was collected and analyzed. The results show that both the light CNN and the CRNN achieve better performance than the pre-trained baseline models. The CRNN was implemented in real time on commodity hardware for use in the cannulation simulator, and the performance was verified. Deep-learning video analysis is a viable method for detecting needle state in a low cost cannulation simulator. Our data sets and code are released at https://github.com/axin233/DL_for_Needle_Detection_Cannulation