Intelligent Medical Scanner Adaptation via Learning Model
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Solution Overview
Problem
Conventional medical imaging scanners rely heavily on operator expertise and pre-installed protocols, which are not robust enough to handle diverse diagnostic requirements, leading to suboptimal scan quality and difficulty in adjusting settings for new conditions.
Innovation Solution
An intelligent medical imaging scanner system that includes an image scanner, operator interface, database, and processors, utilizing a learning model to generate configurations based on input requirements, with natural language processing and feature extraction to refine parameters, and an accommodation process to modify settings when necessary, mimicking human intellectual development processes like assimilation and accommodation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional scanners use pre-installed standard protocols, then the scanning process is standardized and easy to operate, but the protocols are not robust enough to apply to all conditions and cannot handle diverse diagnostic requirements
Solution Approach 1:
The scanner system performs self-learning and self-configuration through the learning model that automatically adapts scanning parameters and protocols based on diagnostic requirements, eliminating the need for operator expertise in protocol selection and parameter adjustment
Solution Approach 2:
The system implements feedback mechanisms where scan results and diagnostic outcomes are fed back into the learning model to continuously improve and refine scanning configurations, enabling the system to adapt to diverse diagnostic requirements while maintaining ease of operation
2Adaptability or versatility
If operators manually adjust scanner settings for new diagnostic requirements, then customized scans can be achieved, but it is difficult for operators to search for optimal solutions and adjust settings efficiently
Solution Approach 1:
The learning model pre-configures optimal scanning parameters and protocols based on diagnostic requirements before actual scanning occurs, eliminating the time-consuming process of manual parameter adjustment and allowing operators to quickly initiate customized scans
Solution Approach 2:
The system replaces manual operator adjustment with an intelligent learning model that automatically optimizes scanning configurations, substituting human cognitive processes with computational algorithms that can rapidly evaluate and select optimal parameters
3Measurement precision
If intelligent scanners use post-processing techniques to augment image quality, then image quality and imaging speed are improved, but the system still highly relies on operator expertise to determine protocols and organize workflows
Solution Approach 1:
The learning model serves multiple functions including protocol selection, parameter optimization, and workflow organization in a single integrated system, eliminating the need for separate expert knowledge for each function while maintaining high image quality through coordinated post-processing techniques
Data Source
AI summary
An intelligent medical imaging scanner system includes an image scanner, an operator interface, a database, processors, and a storage medium. The database includes a learning model for relating configurations of the image scanner to operator input requirements. The storage medium contains programming instructions that, when executed, cause the processors to determine whether the learning model may be used to generate a configuration of the image scanner corresponding to the new input requirements. If the configuration can be generated, the processors use that configuration to acquire images of a patient using the image scanner. If the configuration of the image scanner cannot be generated, the processors perform an accommodation process comprising (a) modifying the learning model to generate a new configuration of the image scanner corresponding to the new input requirements, and (b) using the new configuration to acquire the images of the patient using the image scanner.