US 12,109,434 B2
Neural network calibration for radiotherapy
Mikko Hakala, Rajamaki (FI); Esa Kuusela, Espoo (FI); Elena Czeizler, Helsinki (FI); and Shahab Basiri, Siuntio (FI)
Assigned to Siemens Healthineers International AG, Steinhausen (CH)
Filed by Siemens Healthineers International AG, Steinhausen (CH)
Filed on Dec. 1, 2023, as Appl. No. 18/527,066.
Application 18/527,066 is a continuation of application No. 18/114,826, filed on Feb. 27, 2023, granted, now 11,865,369.
Application 18/114,826 is a continuation of application No. 17/124,223, filed on Dec. 16, 2020, granted, now 11,590,367.
Prior Publication US 2024/0115883 A1, Apr. 11, 2024
Int. Cl. A61N 5/10 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01)
CPC A61N 5/1064 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
retrieving, by one or more processors via a radiation therapy treatment generation (RTTP) model, a set of treatment attribute predictions for a radiotherapy treatment of a patient;
executing, by the one or more processors, a calibration model using the set of treatment attribute predictions and a set of labels indicating expected treatment attribute predictions to predict a calibration value; and
revising, by the one or more processors, at least one configuration of the RTTP model using the calibration value.