Using machine-learning language processing model for radiotherapy visualization

Patent metadata

Field	Value
Publication number	US-2025372231-A1
Application number	US-202418732529-A
Country	US
Kind code	A1
Filing date	Jun 3, 2024
Priority date	Jun 3, 2024
Publication date	Dec 4, 2025
Grant date	—

Abstract

Disclosed herein are methods and systems for generating visualizations of radiation therapy treatments utilizing machine learning. One method involves presenting a user interface on a user device, designed for medical professionals, comprising an interaction interface. Through this interface, the processor receives one or more visualization attributes associated with a patient's radiation therapy treatment. Subsequently, a machine-learning model is executed by the processor, utilizing the received visualization attributes to generate machine-readable code. This code instructs a visualization software module to generate a visualization corresponding to the provided attributes. Upon generation, the machine-readable code is transmitted to the software module, which responds by presenting the visualization via the user interface. This method enhances the efficiency and accuracy of visualizing radiation therapy treatments, facilitating informed decision-making by medical professionals.

First claim

What we claim is:

1 . A method comprising:
presenting, by a processor on a user device, a user interface comprising an interaction interface for a medical professional operating the user device; receiving, by the processor from the interaction interface, one or more visualization attributes associated with a radiation therapy treatment of a patient; executing, by the processor, a machine-learning language processing model using the one or more visualization attributes to generate machine readable code instructing a visualization software module to generate a visualization corresponding to the one or more visualization attributes; transmitting, by the processor, the machine-readable code, to the software module; and in response to receiving the visualization from the visualization software module, presenting, by the processor, the visualization via the user interface.

2 . The method of claim 1 , wherein the machine-readable code comprises a digital library to generate the visualization.

3 . The method of claim 1 , wherein the machine-learning language processing model is specifically trained for a plan optimizer that generates the radiation therapy treatment of the patient.

4 . The method of claim 1 , wherein the machine-learning language processing model is specifically trained for the visualization using at least one configuration of the visualization engine.

5 . The method of claim 1 , further comprising:
recalibrating, by the processor, the machine-learning language processing model using an input corresponding to a quality of the visualization.

6 . The method of claim 1 , further comprising:
adjusting, by the processor, an attribute of the treatment plan in accordance with an input received after presenting the visualization.

7 . The method of claim 1 , wherein the visualization is a comparison between an attribute of the treatment plan at two different times.

8 . A system comprising:
a non-transitory computer readable medium having instructions that when executed, cause a processor to:
present, on a user device, a user interface comprising an interaction interface for a medical professional operating the user device;
receive, from the interaction interface, one or more visualization attributes associated with a radiation therapy treatment of a patient;
execute a machine-learning language processing model using the one or more visualization attributes to generate machine readable code instructing a visualization software module to generate a visualization corresponding to the one or more visualization attributes;
transmit the machine-readable code, to the software module; and
in response to receiving the visualization from the visualization software module, present the visualization via the user interface.

9 . The system of claim 8 , wherein the machine-readable code comprises a digital library to generate the visualization.

10 . The system of claim 8 , wherein the machine-learning language processing model is specifically trained for a plan optimizer that generates the radiation therapy treatment of the patient.

11 . The system of claim 8 , wherein the machine-learning language processing model is specifically trained for the visualization using at least one configuration of the visualization engine.

12 . The system of claim 8 , wherein the instructions further cause the processor to recalibrate the machine-learning language processing model using an input corresponding to a quality of the visualization.

13 . The system of claim 8 , wherein the instructions further cause the processor to adjust an attribute of the treatment plan in accordance with an input received after presenting the visualization.

14 . The system of claim 8 , wherein the visualization is a comparison between an attribute of the treatment plan at two different times.

15 . A system comprising:
a machine-learning language processing model; and a processor in communication with the machine-learning language processing model, the processor configured to:
present, on a user device, a user interface comprising an interaction interface for a medical professional operating the user device;
receive, from the interaction interface, one or more visualization attributes associated with a radiation therapy treatment of a patient;
execute the machine-learning language processing model using the one or more visualization attributes to generate machine readable code instructing a visualization software module to generate a visualization corresponding to the one or more visualization attributes;
transmit the machine-readable code, to the software module; and
in response to receiving the visualization from the visualization software module, present the visualization via the user interface.

16 . The system of claim 14 , wherein the machine-readable code comprises a digital library to generate the visualization.

17 . The system of claim 14 , wherein the machine-learning language processing model is specifically trained for a plan optimizer that generates the radiation therapy treatment of the patient.

18 . The system of claim 14 , wherein the machine-learning language processing model is specifically trained for the visualization using at least one configuration of the visualization engine.

19 . The system of claim 14 , wherein the processor is further configured to recalibrate the machine-learning language processing model using an input corresponding to a quality of the visualization.

20 . The system of claim 14 , wherein the visualization is a comparison between an attribute of the treatment plan at two different times.

Assignees

• Siemens Healthineers Int Ag

Inventors

• Peltola Jarkko
• Kuusela Esa
• Hautala Ismo

Classifications

• G16H40/67

  for remote operation · CPC title

• G16H20/40 Primary

  relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08

  Learning methods · CPC title

• A61N5/103

  Treatment planning systems · CPC title