AI Radiotherapy Visualization for Custom Treatment Planning

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Solution Overview

Problem

Current radiotherapy treatment planning software is cumbersome, featuring outdated interfaces and limited customization options, hindering medical professionals' ability to make informed decisions and adapt treatments to patient-specific needs, and is constrained by individual skills and software limitations.

Innovation Solution

A machine-learning language processing model is employed to generate real-time or near-real-time visualizations tailored to user needs, enabling enhanced treatment plan visualization through an AI visualization assistant that integrates with existing software systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If traditional visualization software is used, then the treatment plan can be visualized, but the process is cumbersome and the interface is outdated, reducing ease of operation

Engineering Contradiction:

Improveease of operationVSAvoidsoftware complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent introduces an AI assistant as an intermediary between the medical professional and the complex treatment planning system. The AI assistant handles complex queries and visualization requests, translating natural language inputs into appropriate visualizations without requiring the user to navigate complex software interfaces directly.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service through the AI assistant, which automatically generates visualizations and answers queries without requiring manual navigation through complex software menus. The AI assistant autonomously interprets user intent and executes appropriate visualization commands.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If existing visualization software capabilities are used, then standard visualizations can be generated, but customization options are limited, reducing adaptability

Engineering Contradiction:

Improvecustomization capabilityVSAvoidsoftware configuration

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic adaptability where the AI assistant can adjust visualization parameters and types in real-time based on user feedback and specific query requirements. The system transitions from static, pre-defined visualization options to dynamic, on-demand customization without requiring complex software configuration.

Inventive Principle:

Principle #15Dynamics

3Productivity

If medical professionals manually operate visualization software, then visualizations can be generated, but the process is time-consuming, reducing productivity

Engineering Contradiction:

Improvevisualization generation speedVSAvoidtime for treatment planning

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The AI assistant performs preliminary actions by anticipating user needs and pre-generating relevant visualizations or information before explicit requests are made. The system proactively prepares treatment plan visualizations and answers common queries, reducing the time users need to wait for information.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual mechanical interaction with visualization software (clicking through menus, adjusting parameters manually) with an AI-based system that processes natural language queries and automatically generates visualizations, significantly reducing the time required to obtain treatment plan information.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Reliability

If standard treatment protocols are followed, then treatment plans can be created, but the ability to adapt to patient-specific needs is reduced, lowering treatment effectiveness

Engineering Contradiction:

Improvetreatment plan qualityVSAvoidpatient-specific adaptation

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The AI assistant enables local quality by allowing customization of treatment plan visualizations and recommendations based on specific patient characteristics, tumor properties, and individual anatomical features. The system maintains standard protocol reliability while adapting to local patient-specific requirements through targeted queries and personalized visualization generation.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250372231A1Using machine-learning language processing model for radiotherapy visualization

Publication Date: 2025.12.04 SIEMENS HEALTHINEERS INTERNATIONAL AG

AI summary

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.