| CPC G16H 20/40 (2018.01) [A61B 5/0522 (2013.01); A61B 5/055 (2013.01); A61N 1/025 (2013.01); G06F 18/2148 (2023.01); G06F 18/241 (2023.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G16H 10/60 (2018.01); G16H 20/30 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61N 1/0476 (2013.01); A61N 1/36002 (2017.08); G01R 33/5608 (2013.01); G06T 2207/30024 (2013.01)] | 20 Claims |

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1. A method comprising:
determining a plurality of sets of image data associated with a plurality of patients, wherein each patient is associated with a set of image data derived from imaging a portion of the patient, wherein each set of image data comprises a plurality of voxels, wherein each voxel of the plurality of voxels is labeled with a tissue type, and wherein each voxel of the plurality of voxels is labeled with an electrical field strength distribution value derived from a simulated application of an alternating electrical field from a pair of transducer arrays to the portion of the patient;
determining, based on a first portion of the plurality of sets of image data, a plurality of features for a predictive model;
training, based on the plurality of features and the first portion of the plurality of sets of image data, the predictive model, wherein the predictive model is trained to estimate electric field strength distribution values;
testing, based on a second portion of the plurality of sets of image data, the predictive model; and
outputting, based on the testing, the predictive model.
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