Abstract: A soft tissue emulation system, comprising: an input interface, configured to obtain imaging data of the soft tissue; a computing unit, configured to implement an artificial neural network, which is adapted to generate, using the obtained imaging data as input, and a biophysical model of the soft tissue, a digital twin of the soft tissue at different times, wherein the biophysical model describes the response of the soft tissue to at least one of thermal stimuli or electromechanical stimuli over time, and wherein the generation of the digital twin at one time is independent of the generation of the digital twin at another time; and an output interface, configured to output a representation of the soft tissue over time based on the digital twin generated by the artificial neural network.

Abstract: Systems and methods for automatically detecting a disease in medical images are provided. Input medical images are received. A plurality of metrics for a disease is computed for each of the input medical images. The input medical images are clustered into a plurality of clusters based on one or more of the plurality of metrics to classify the input medical images. The plurality of clusters comprise a cluster of one or more of the input medical images associated with the disease and one or more clusters of one or more of the input medical images not associated with the disease. In one embodiment, the disease is COVID-19 (coronavirus disease 2019).

Abstract: For soft tissue deformation prediction, a biomechanical or other tissue-related physics model is used to find an instantaneous state of the soft tissue. A machine-learned artificial neural network is applied to predict the position of volumetric elements (e.g., mesh node) from the instantaneous state. Since the machine-learned artificial neural network may predict quickly (e.g., in a second or less), the soft tissue position at different times or a further time given the instantaneous state is provided in real-time without the minutes of physics model computation. For example, a real-time, biomechanical solver is provided, allowing interaction with the soft tissue model, while still getting accurate results. The accuracy allows for generating images of a soft tissue with greater accuracy and/or the benefit of user interaction in real-time.

Abstract: For soft tissue deformation prediction, a biomechanical or other tissue-related physics model is used to find an instantaneous state of the soft tissue. A machine-learned artificial neural network is applied to predict the position of volumetric elements (e.g., mesh node) from the instantaneous state. Since the machine-learned artificial neural network may predict quickly (e.g., in a second or less), the soft tissue position at different times or a further time given the instantaneous state is provided in real-time without the minutes of physics model computation. For example, a real-time, biomechanical solver is provided, allowing interaction with the soft tissue model, while still getting accurate results. The accuracy allows for generating images of a soft tissue with greater accuracy and/or the benefit of user interaction in real-time.