US 12,073,948 B1
Systems and methods for training a model to predict survival time for a patient
Andrew H. Beck, Brookline, MA (US); and Aditya Khosla, Lexington, MA (US)
Assigned to PathAI, Inc., Boston, MA (US)
Filed by PathAI, Inc., Boston, MA (US)
Filed on May 19, 2023, as Appl. No. 18/320,382.
Application 18/320,382 is a continuation of application No. 17/328,057, filed on May 24, 2021, granted, now 11,657,505.
Application 17/328,057 is a continuation of application No. 16/857,079, filed on Apr. 23, 2020, granted, now 11,017,532.
Application 16/857,079 is a continuation of application No. 16/001,836, filed on Jun. 6, 2018, granted, now 10,650,929.
Claims priority of provisional application 62/515,772, filed on Jun. 6, 2017.
Claims priority of provisional application 62/515,779, filed on Jun. 6, 2017.
Claims priority of provisional application 62/515,795, filed on Jun. 6, 2017.
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 50/30 (2018.01); G06F 16/58 (2019.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 5/046 (2023.01); G06T 7/00 (2017.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)
CPC G16H 50/30 (2018.01) [G06F 16/5866 (2019.01); G06F 18/214 (2023.01); G06F 18/2193 (2023.01); G06N 5/046 (2013.01); G06T 7/0014 (2013.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30004 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
determining whether a trained model accurately identifies a patient subset that is likely to benefit from an experimental treatment compared to a standard treatment or a control treatment, wherein the determining comprises:
processing, using the trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated pathology images associated with a first group of patients, to predict survival data for patients in the first group of patients, wherein the first group of patients belongs to an experimental treatment group of a randomized controlled clinical trial;
processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated pathology images associated with a second group of patients, to predict survival data for patients in the second group of patients, wherein the second group of patients belongs to a control treatment group of the randomized controlled clinical trial; and
determining whether the trained model accurately identifies a patient subset that is likely to benefit from the experimental treatment compared to the standard treatment or the control treatment based on the predicted survival data for the patients in the first group of patients and the predicted survival data for the patients in the second group of patients.