Preference-Aware AI Learning for Consistent Radiotherapy Segmentation
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Solution Overview
Problem
Existing image segmentation methods in radiotherapy planning suffer from variability due to differences in institutional, personal, and regional preferences, leading to uncertainty and inconsistency in PTV and OAR delineation, which can result in human error and suboptimal treatment plans.
Innovation Solution
A preference-aware AI model is developed to learn and reproduce various segmentation preferences by training multiple experts, allowing customization based on institutional, personal, and regional guidelines, and enabling flexible segmentation of medical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual image segmentation is performed by medical professionals, then personal expertise and judgment can be applied, but variability and inconsistency in segmentation results occur due to different preferences and interpretations
Solution Approach 1:
The patent segments the segmentation task by training multiple AI experts, where each expert is specialized in segmenting a specific organ or structure. This allows the system to maintain specialized knowledge for different anatomical structures while ensuring consistent application of segmentation guidelines through the AI model's uniform processing approach.
Solution Approach 2:
The patent changes the parameter of segmentation approach from human-dependent to AI-driven by training neural network experts on curated training data that encodes institutional guidelines. This transformation allows the system to consistently apply segmentation parameters across different cases while maintaining adaptability through the learned expertise of multiple AI specialists.
2Extent of automation
If AI models are trained using existing manual segmentation data, then automation is achieved, but variability in training data from different institutions and practitioners propagates to the AI model
Solution Approach 1:
The patent performs preliminary action by carefully curating training data before AI model training. Training datasets are specifically selected and prepared to follow a single set of contouring guidelines, ensuring that the AI experts learn from consistent, high-quality data that reflects institutional preferences without the noise and variability present in general manual segmentation data.
3Measurement precision
If training data is curated to follow a single set of contouring guidelines, then consistency in AI model training is achieved, but institutional and personal preferences are removed from the dataset
Solution Approach 1:
The patent applies local quality by creating different AI experts specialized in different organs or structures, where each expert can be trained on data reflecting specific institutional preferences for that particular anatomical region. This allows the system to maintain local specialization and preference adaptation while ensuring overall consistency through the structured expert framework.
4Adaptability or versatility
If AI models are trained to converge to an average structure, then generalization is improved, but institutional and personal segmentation preferences are not considered
Solution Approach 1:
The patent segments the AI model into multiple specialized experts rather than using a single generalist model. Each expert is trained to achieve high precision for specific organs or structures while maintaining institutional preferences, eliminating the need to converge to an average structure that would compromise segmentation quality.
Data Source
AI summary
Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.