Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles

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 spatio-temporal 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.

Multilayer spintronic neural networks with radiofrequency connections

Spintronic nano-synapses and nano-neurons perform neural network operations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided they implement state-of-the-art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radiofrequency signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly separable radiofrequency inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of-the-art identification of drones from their radiofrequency transmissions, without digitization and consuming only a few milliwatts, which constitutes a gain of several orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.

A Novel Local Tumor Progression Prediction Method for Multimode Ablation Treatment

OBJECTIVE

The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis.

METHODS

We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction.

RESULTS

We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.8550.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance.

CONCLUSION

The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy.

SIGNIFICANCE

This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.