Predicting spatio-temporal radiofrequency ablation temperature using deep neural networks

Radiofrequency ablation (RFA) of the medial branch nerve is a widely used therapeutic intervention for facet joint pain. However, denervation of the multifidus muscle is an inevitable consequence of RFA. New ablation techniques with the potential to prevent muscle denervation can be designed using computational simulations. However, depending on the complexity of the model, they could be computationally expensive. As an alternative approach, deep neural networks (DNNs) can be used to predict tissue temperature during RFA procedure. The objective of this paper is to predict the tissue spatial and temporal temperature distributions during RFA using DNNs. First, finite element (FE) models with a range of distances between the probes were run to obtain the temperature readings. The measured temperatures were then used to train the DNNs that predict the spatiotemporal temperature distribution within the tissue. Finally, a separate data obtained from FE simulations were used to test the efficacy of the network. The results presented in this paper demonstrate that the network can achieve an error rate as low as 0.05%, accompanied by a 92% reduction in time compared to FE simulations. The approach proposed in this study will play a major role in the design of new RFA treatments for facet joint pain.