Medical Imaging Component Selection Using LLM-Guided Sorting
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Solution Overview
Problem
The challenge in constructing medical imaging apparatuses lies in efficiently selecting and optimizing individual components, such as detector modules, to ensure, to achieve consistent image quality, as existing technologies fail to address the challenge of achieving uniform image quality due to varying production and measurement tolerances, leading to numerous possible combinations that are not effectively addressed by existing methods.
Innovation Solution
The method employs machine learning algorithms, specifically neural networks, to generate optimized component selection and sorting, utilizing machine learning algorithms to optimize the selection of individual components, such as detector modules, by employing text inputs, and, in particular, natural language prompts to guide the sorting process, ensuring efficient and rapid selection and placement of components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual selection and sorting of individual components is performed, then some level of quality control is achieved, but the process is extremely time-consuming and cannot handle the vast number of possible combinations
Solution Approach 1:
The patent replaces manual mechanical selection and sorting processes with an automated machine learning system. The neural network algorithm automatically evaluates component properties, predicts image quality outcomes, and generates optimized sorting sequences, eliminating the need for time-consuming manual inspection and assembly while maintaining or improving quality consistency.
Solution Approach 2:
The system transforms the selection problem from a combinatorial optimization challenge into a parameter-based prediction task. By using machine learning models to predict image quality based on component parameters and their interactions, the system efficiently navigates the vast combination space without exhaustively evaluating each possibility, dramatically reducing selection time while maintaining precision.
2Manufacturing precision
If the number of detector modules in inventory is increased to provide more selection possibilities, then the ability to optimize image quality improves, but the number of possible combinations increases exponentially making selection intractable
Solution Approach 1:
The patent extracts the essential predictive relationships from training data and encodes them into a machine learning model. This model captures the complex interactions between component properties and image quality outcomes, allowing the system to evaluate new combinations rapidly without needing to physically assemble and test each possibility, thus managing complexity while maintaining optimization capability.
Solution Approach 2:
The system performs preliminary training and validation using historical component data and known good configurations. This pre-learning phase enables the model to understand component behavior patterns before actual production sorting, allowing rapid evaluation of new combinations during manufacturing without repeating the full optimization process each time.
3Manufacturing precision
If traditional sorting methods are used to ensure consistent image quality, then quality control is maintained, but the process cannot adapt quickly to changes in component inventory or quality standards
Solution Approach 1:
The patent implements a dynamic sorting system where the machine learning model can be retrained and reconfigured based on changing inventory conditions, quality standards, or new component batches. The system adapts to new data and requirements by updating its predictive models, allowing flexible response to changing manufacturing conditions while maintaining consistent quality output.
Solution Approach 2:
The system incorporates feedback mechanisms where actual image quality measurements from assembled detectors are fed back into the training data. This continuous learning loop allows the model to refine its predictions and adapt to real-world variations in component performance, improving both consistency and adaptability over time.
Data Source
AI summary
One or more example embodiments relates to a method for selecting individual components of a medical imaging apparatus by selecting a group of individual components from a component set by way of an algorithm for machine learning. A text input and/or a prompt is provided. The text input is used by the algorithm in the selection of the group of individual components. With the text input, the selection and/or sorting can be specified very easily.