Reinforcement Learning for Data-Efficient Radiation Therapy Planning
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Solution Overview
Problem
Conventional radiation therapy treatment planning (RTTP) is inefficient, time-consuming, and prone to errors due to reliance on subjective clinician interpretation and computationally intensive AI training methods that require large datasets.
Innovation Solution
A training system using reinforcement learning iteratively trains an AI model with a limited dataset, balancing exploration and exploitation to predict treatment attributes, combining reinforcement and supervised learning to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AI training methods are used for radiation therapy treatment planning, then the model can learn from historical data, but the training process is computationally intensive, costly, and requires a long training period with large datasets
Solution Approach 1:
The system performs preliminary action by pre-defining multiple possible treatment attributes and their relationships before the actual training process. The treatment planning system pre-processes historical treatment data to create a structured knowledge base of treatment attributes, constraints, and relationships, which serves as a foundation for the AI model training and significantly reduces the training time required.
Solution Approach 2:
The system segments the treatment planning process into distinct attribute categories (e.g., beam parameters, patient-specific parameters, treatment constraints) that can be independently analyzed and trained. This segmentation allows the AI model to learn from smaller, more manageable data subsets rather than requiring processing of entire large-scale datasets simultaneously.
2Measurement precision
If conventional AI training methods are used for radiation therapy treatment planning, then the model can predict treatment attributes, but the training process is computationally intensive and costly
Solution Approach 1:
The system applies partial action by training the AI model on selected subsets of treatment attributes rather than requiring complete training on all possible attributes simultaneously. The system identifies and prioritizes the most critical treatment attributes for accurate prediction, training the model on these essential parameters first, which reduces computational resources required while maintaining prediction accuracy for the most important clinical decisions.
Solution Approach 2:
The system changes parameters by transforming the training approach from conventional exhaustive training to a parameter-efficient training method. The predefined treatment attributes and their relationships serve as fixed parameters that guide the training process, allowing the model to learn with fewer computational iterations and reduced resource requirements while maintaining predictive accuracy.
3Measurement precision
If conventional AI training methods are used for radiation therapy treatment planning, then the model can learn from historical data, but it requires a large number of training data points
Solution Approach 1:
The system uses copying by creating synthetic training data through the predefined treatment attribute relationships. Instead of requiring large volumes of real historical treatment data, the system generates additional training examples by copying and varying the relationships between treatment attributes from existing cases, effectively expanding the training dataset without requiring proportional increases in actual clinical data collection.
Solution Approach 2:
The system achieves universality by creating a training framework based on universal treatment attribute relationships that can be applied across different treatment scenarios and patient cases. The predefined treatment attributes and their interrelationships form a universal knowledge structure that allows the AI model to learn from smaller datasets while maintaining the ability to generalize to new treatment situations.
Data Source
AI summary
Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates, using an existing policy, a first predicted radiation therapy treatment attribute, and generates, using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward. The agent iteratively repeats training the artificial intelligence model until the existing policy satisfies an accuracy threshold.