AI-Assisted Circuit Digital Twin Reproducing Ultrasound Waves in Human Tissues

The paper proposes a Digital Twin (DTw) framework, constructing a circuit model replicating the pulse transmission and reception processes for devices with high sensitivity to noises, such as wearable ultrasound transducers. The model is suitable to train supervised AI algorithms denoising the noisy ultrasound signal received. The DTw combines the circuit simulations with the AI data processing by training the model with the cleaned pulsed signals and by correcting the noises modeled by ‘white-noise’ voltage generators. Specifically, the voltage outputs of the circuit simulations are used to train the AI models and to test noisy signals for reconstruction. The DTw model is based on the transmission line theory combined with the perturbation impedance approach, supporting human body tissue discrimination based on noises. Two open-source tools are used for the DTw construction, the LTSpice and the Orange Mining tool, which are used for the circuit simulation and for the AI data processing, respectively. The theoretical work proves that the methodology is able to reconstruct correctly, with a good performance in the time domain and the frequency domain, noisy voltage signals, by addressing the analysis on cancer detection by combining circuit, AI and Monte Carlo approaches.