Treatment Planning Hub for AI Tool Integration and Data Matching
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Solution Overview
Problem
Current radiation therapy systems lack integration and efficiency in using AI/machine learning models for patient treatment planning, leading to isolated usage and inefficient data handling across different treatment platforms, which complicates the process and may result in suboptimal treatment plans.
Innovation Solution
A system and method that integrates multiple treatment planning tools through a hub interface, converting data formats to a standard, enabling seamless communication and automatic initiation of actions based on patient data availability, facilitating efficient use of AI/machine learning models for segmentation, beam placement, and radiotherapy planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple treatment planning tools are integrated through a hub interface with data format conversion, then data handling efficiency and treatment planning effectiveness are improved, but device complexity increases
Solution Approach 1:
A hub interface is introduced as an intermediary component between patient data sources and multiple treatment planning tools. This hub performs data format conversion and standardization, enabling seamless communication between heterogeneous systems without requiring direct integration between each tool pair, thus improving efficiency while managing complexity through a centralized mediation layer.
Solution Approach 2:
The hub interface is designed with universal data conversion capabilities that can handle multiple data formats and protocols from different treatment planning tools. This multi-functional design allows a single system component to serve various tools with different requirements, improving overall system productivity without proportionally increasing complexity.
2Reliability
If AI/machine learning models are used for treatment planning optimization, then treatment plan quality is improved, but data handling complexity and processing time increase
Solution Approach 1:
The system performs preliminary data standardization and preparation through the hub interface before data is passed to AI/machine learning models. By pre-processing and standardizing data formats in advance, the system reduces the computational burden during optimization, thereby improving treatment plan quality while minimizing additional processing time.
3Extent of automation
If data formats are standardized across different treatment platforms, then system integration and automation are improved, but adaptability to different tool formats decreases
Solution Approach 1:
The hub interface dynamically changes data format parameters based on the requirements of different treatment planning tools. It maintains a standardized internal representation for automation while performing parameter transformations when interfacing with specific tools, thus enabling automatic execution without sacrificing adaptability to diverse tool formats.
Data Source
AI summary
A system for facilitating patient treatment planning includes a processor coupled to a first interface and a second interface. The first interface is configured to communicate with a patient data source to receive data relating to a patient. The second interface is configured to communicate with a treatment planning tool data source to receive data relating to one or more treatment planning tools. The processor is configured to: determine whether the data relating to the patient matches data required by one or more of the treatment planning tools; and automatically initiate an action in response to the data relating a patient matches the data required by one or more of the treatment planning tools.