Key result
Deep neural networks successfully estimated contact force from a newly collected benchmark dataset, achieving an average error of 2.46g for RNN, 3.03g for FCN, and 3.01g for Transformer.
Why the study?
The practical usability of commercialized contact force sensing systems during catheter ablation therapy is not fully understood due to measurement inaccuracies.
Deep learning models, particularly RNNs, can accurately estimate contact force from FBG sensors in ablation catheters, overcoming the Shift of Reference problem.
Answers are grounded in this paper and cite their sources.
May enhance catheter positioning accuracy in ablation; leaves open need for prospective clinical validation.
With rise of interventional cardiology, Catheter Ablation Therapy (CAT) has established itself as a first-line solution to treat cardiac arrhythmia. Although CAT is a promising technique, cardiologist lacks vision inside the body during the procedure, which may cause serious clinical syndromes. To support accurate clinical procedure, Contact Force Sensing (CFS) system is developed to find a position of the catheter tip through the measure of contact force between catheter and heart tissue. However, the practical usability of commercialized CFS systems is not fully understood due to inaccuracy in the measurement. To support the development of more accurate system, we develop a full pipeline of CFS system with newly collected benchmark dataset through a contact force sensing catheter in simplest hardware form. Our dataset was roughly collected with human noise to increase data diversity. Through the analysis of the dataset, we identify a problem defined as Shift of Reference (SoR), which prevents accurate measurement of contact force. To overcome the problem, we conduct the contact force estimation via standard deep neural networks including for Recurrent Neural Network (RNN), Fully Convolutional Network (FCN) and Transformer. An average error in measurement for RNN, FCN and Transformer are, respectively, 2.46g, 3.03g and 3.01g. Through these studies, we try to lay a groundwork, serve a performance criteria for future CFS system research and open a publicly available dataset to public.
No takes yet. Share an insight, caveat, or question.
Cho et al. (2021) studied Cardiac arrhythmia (context for catheter ablation). Deep neural networks (RNN, FCN, Transformer) for contact force estimation was evaluated on Average error in contact force measurement (Mean Absolute Error). Deep neural networks successfully estimated contact force from a newly collected benchmark dataset, achieving an average error of 2.46g for RNN, 3.03g for FCN, and 3.01g for Transformer.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: