US 12,424,328 B2
Comorbidity prediction from radiology images
Oluwasanmi Koyejo, Urbana, IL (US); Andrew Chen, Urbana, IL (US); Patrick Cole, Urbana, IL (US); Nasir Siddiqui, Hinsdale, IL (US); and Ayis Pyrros, Hinsdale, IL (US)
Assigned to The Board of Trustees of the University of Illinois, Urbana, IL (US)
Filed by The Board of Trustees of the University of Illinois, Urbana, IL (US)
Filed on Jul. 11, 2022, as Appl. No. 17/861,347.
Claims priority of provisional application 63/220,315, filed on Jul. 9, 2021.
Prior Publication US 2023/0016569 A1, Jan. 19, 2023
Int. Cl. G16H 50/30 (2018.01); G06T 7/00 (2017.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G16H 50/30 (2018.01) [G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving a target radiographic image of a chest of a subject, wherein the subject has a specified disease or syndrome, wherein an artificial neural network (ANN) has a set of inputs and a set of outputs, wherein the ANN has been trained by a training set including a plurality of radiographic images containing features that are mapped to the set of inputs and associated with labeled values mapped to the set of outputs, wherein a subset of the radiographic images contain instances of the features that are indicative of at least one comorbidity within a particular comorbidity class and are associated with instances of the labeled values that represent the particular comorbidity class, wherein the ANN a common layer and a plurality of parallel output heads, wherein the common layer provides outputs to the plurality of parallel output heads, and wherein each output head of the plurality of parallel output heads generates, as an output, a respective one of the set of outputs;
applying the ANN to the target radiographic image to generate a set of target outputs for the target radiographic image, wherein output values of the set of target outputs indicate that the subject has at least one comorbidity in the particular comorbidity class;
based on the set of target outputs for the target radiographic image, predicting at least one of (i) a degree of severity of the specified disease or syndrome or (ii) an amount of care that the subject will receive related to the specified disease or syndrome;
based on the set of target outputs for the target radiographic image, predicting a likelihood that the subject has a specified disease or syndrome;
determining that the likelihood that the subject has the specified disease or syndrome has an intermediate value; and
responsive to determining that the likelihood that the subject has the specified disease or syndrome has an intermediate value, performing an additional diagnostic test on the subject to determine whether or to what degree the subject has the specified disease or syndrome.