AI Imaging Protocol Generation for Patient-Specific Scan Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing medical imaging protocols are inefficient and require significant user intervention due to non-standardized physician instructions and lack of automated consideration of patient-specific and equipment-specific parameters, leading to inconsistencies and suboptimal procedure execution.
Innovation Solution
A computer-implemented method using a trained language model to generate or modify imaging protocols based on input data sets, including patient information, medical instructions, and equipment specifications, ensuring consistency and optimizing procedure parameters through artificial intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual protocol generation is used, then user control and flexibility are maintained, but time consumption and resource expenditure increase significantly
Solution Approach 1:
The system enables self-service protocol generation where the language model automatically creates imaging protocols based on input data sets without requiring manual intervention. The model processes patient information, medical instructions, and equipment specifications to generate optimized protocols autonomously, significantly reducing the time and resources needed for protocol creation while maintaining quality through AI-based optimization
2Reliability
If standardized protocols are used, then consistency is improved, but adaptability to patient-specific and equipment-specific conditions deteriorates
Solution Approach 1:
The system applies local quality by generating protocol parameters that are specifically tailored to individual patients and equipment conditions. Instead of using a one-size-fits-all standardized protocol, the language model adjusts imaging parameters based on patient-specific factors (age, weight, condition) and equipment specifications, ensuring each protocol is optimized for its specific context while maintaining overall consistency through AI-generated standardization
3Productivity
If automated protocol generation is implemented, then time and resource consumption are reduced, but complexity of the system increases
Solution Approach 1:
The language model serves as an intermediary between raw input data (patient information, medical instructions, equipment specs) and the final protocol output. This intermediary component handles the complexity of processing and transforming diverse data formats into coherent protocol parameters, shielding users from system complexity while delivering automated protocol generation benefits
Data Source
Figure 1~2
AI summary
Computer-implemented method for generating a protocol and/or output aimed at generating or modifying a protocol, medical device, computer-readable storage medium, and computer-implemented method for generating a trained model. The invention relates to a computer-implemented method for generating a protocol (9) and/or output (23) aimed at generating or modifying a protocol (9), wherein the protocol (9) represents an implementation instruction for controlling the execution of a medical imaging procedure using a medical imaging device (5) and specifies at least one variable parameter (18, 19) of the imaging procedure for its execution, wherein the method comprises the following steps: - specifying at least one input data set (10, 12, 14, 15, 16, 22, 25),which relates to at least one specification for the imaging procedure, - evaluating the at least one input data set (10, 12, 14, 15, 16, 22, 25), wherein, depending on the result of this evaluation, the protocol (9) and/or the output (23) is generated, wherein this evaluation is carried out using a trained language model (8) generated by machine learning.