The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 30, 2025

Filed:

May. 15, 2019
Applicant:

Elekta Ab (Publ), Stockholm, SE;

Inventors:

David Andreas Tilly, Uppsala, SE;

Stella Lucie Riad, Sundbyberg, SE;

Peter Kimstrand, Uppsala, SE;

Nina Terese Tilly, Uppsala, SE;

Klas Marcks Von Würtemberg, Stockholm, SE;

Assignee:

Elekta AB (publ), Stockholm, SE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61N 5/10 (2006.01); A61B 5/00 (2006.01); G06N 20/00 (2019.01); G16H 20/40 (2018.01);
U.S. Cl.
CPC ...
A61N 5/1067 (2013.01); A61B 5/7267 (2013.01); A61N 5/1036 (2013.01); A61N 5/1081 (2013.01); G06N 20/00 (2019.01); G16H 20/40 (2018.01);
Abstract

Techniques for adjusting radiotherapy treatment for a patient in real-time are provided. The techniques include obtaining a training patient anatomy at a first time within a training radiotherapy treatment fraction after a training radiotherapy treatment dose has been delivered by a radiotherapy device; computing a deviation between the training patient anatomy at the first time and reference training patient anatomy during the training radiotherapy treatment fraction, wherein the reference training patient anatomy indicates a prescribed training dose parameter to be delivered within the training radiotherapy treatment fraction; applying the computed deviation to a machine learning model to estimate one or more intra-fraction radiotherapy treatment parameters of a function that provides a radiotherapy device parameter adjustment based on the one or more intra-fraction radiotherapy treatment parameters; and training the machine learning model to establish a relationship between the computed deviation and the one or more intra-fraction radiotherapy treatment parameters.


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