Machine-Learning Plan Optimization for Adaptive Radiation Treatment
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Solution Overview
Problem
Existing automated radiation therapy planning systems require human oversight and are prone to errors and inconsistencies due to time-consuming manual processes, which can lead to suboptimal treatment plans.
Innovation Solution
A plan optimization method utilizing a machine learning model that dynamically updates medical parameters and adjusts treatment manner records based on current medical variables, incorporating causal graph models and continuous time dynamic causal planning graphs to enhance accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated radiation therapy planning algorithms are used, then time required to develop treatment plans is reduced and errors are minimized, but the accuracy and reliability of treatment plans deteriorate due to inability to dynamically adapt to changing patient conditions
Solution Approach 1:
The patent implements a dynamic planning system where the treatment plan is not fixed but continuously updated as new medical variables become available. The system transitions from static automated planning to dynamic adaptive planning, allowing the treatment plan to evolve with changing patient conditions while maintaining automated efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring medical variables and using them to update the treatment plan. New medical variables obtained during treatment are fed back into the planning system, which then adjusts the plan accordingly, creating a closed-loop control system that improves reliability while maintaining automation.
2Reliability
If manual radiation treatment planning is used, then treatment plans can be customized and adjusted, but the process is time-consuming and prone to human error and inconsistency
Solution Approach 1:
The patent introduces an intelligent intermediary system that acts as a bridge between manual planning expertise and automated efficiency. This system captures the decision-making logic of experienced planners and encodes it into algorithms, allowing automated systems to replicate human expertise while eliminating the time-consuming and error-prone aspects of manual planning.
Solution Approach 2:
The patent replaces the mechanical human planning process with an automated computational system. By substituting human operators with algorithms that process medical variables and generate treatment plans, the system eliminates human errors and inconsistencies while dramatically reducing the time required for plan development.
3Device complexity
If treatment plan is fixed based on initial medical variables, then planning process is simplified, but the plan cannot adapt to real-time changes in patient conditions
Solution Approach 1:
The patent transforms the static treatment plan into a dynamic one that can adapt to changing conditions. By implementing a system where new medical variables trigger plan updates, the treatment plan becomes flexible and responsive without requiring complex manual intervention, balancing adaptability with process simplicity.
Data Source
AI summary
A plan optimization method, a computing apparatus for optimizing the plan, and a computer-readable medium are provided. Obtain multiple medical variables at the current time, at least one of the medical variables is different from the medical variables at the previous time, and one of those medical variables at the current time corresponds to a previous state at the previous time. By inputting those medical variables at the current time into the machine learning model, plan information is determined, where the plan information includes at least one subplan information, and each piece of subplan information corresponds to at least one treatment manner record at a subsequent time. Therefore, it could improve the inference accuracy of the model.