Medical Examination Support Apparatus Algorithm Selection
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Solution Overview
Problem
Current medical examination support systems require significant time and effort for doctors to manually select suitable diagnosis support algorithms for multiple medical images, and there is a growing need for a mechanism to automatically select appropriate algorithms without doctor intervention as the types of examination data increase.
Innovation Solution
A medical examination support apparatus and method that includes an instruction receiving unit, an algorithm selection unit, an analysis processing unit, and a screen output control unit to automatically select and apply a suitable diagnosis support algorithm for multiple pieces of examination data, updating the displayed diagnosis support information based on changes in the selection state of the examination data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a doctor manually selects a suitable diagnosis support algorithm for each medical image, then the selection can be customized for each image type, but it takes significant time and effort
Solution Approach 1:
The system automatically selects the suitable diagnosis support algorithm by itself based on the examination data type, eliminating the need for manual doctor intervention. The algorithm selection unit autonomously determines which algorithm (e.g., algorithm A for CT images, algorithm B for MRI images) to apply, making the system self-serving in the algorithm selection process.
Solution Approach 2:
The manual mechanical process of doctor selection is replaced with an automated electronic system. The algorithm selection unit uses automated logic to match examination data types with appropriate diagnosis support algorithms, substituting the mechanical human decision-making process with an electronic automation system.
2Productivity
If a simple selection method is used for single medical images, then the process is fast, but it cannot handle complex multi-image analysis scenarios
Solution Approach 1:
The algorithm selection mechanism is made dynamic to adapt to different scenarios. When a single medical image is selected, the system quickly applies the corresponding algorithm. When multiple different types of medical images are selected, the system dynamically determines to use a comprehensive diagnosis support algorithm that can handle multiple image types, thus adapting the selection process to the complexity of the input.
Solution Approach 2:
The system changes the selection parameters based on the number and types of selected medical images. For single-image scenarios, it uses simple type-matching parameters. For multi-image scenarios involving different examination types, it switches to parameters that identify comprehensive algorithms capable of handling diverse image types, thus changing the selection criteria according to the situation.
3Reliability
If separate diagnosis support algorithms are used for each examination data type, then each algorithm can be optimized for its specific data type, but the system complexity increases
Solution Approach 1:
The system introduces a universal comprehensive diagnosis support algorithm that can handle multiple examination data types (CT, MRI, etc.). This multi-functional algorithm reduces the need for doctors to manually select from multiple specialized algorithms, while still maintaining the ability to provide accurate diagnosis support for different image types through a single unified algorithm.
4Extent of automation
If automatic algorithm selection is implemented, then doctor intervention is reduced, but the system must handle increasing types of examination data
Solution Approach 1:
The system performs preliminary classification of examination data types before algorithm selection. By pre-organizing the characteristics of different examination data types (CT, MRI, etc.) and their corresponding suitable algorithms, the system can quickly and automatically determine the appropriate algorithm without complex real-time analysis, thus handling increasing data types efficiently.
Data Source
AI summary
A request receiving unit of a medical examination support server receives at least two selection instructions among a plurality of pieces of examination data obtained in a medical examination performed on a patient. An algorithm selection unit selects a suitable diagnosis support algorithm according to at least two pieces of the examination data from among the plurality of diagnosis support algorithms. An analysis processing unit performs analysis processing by the suitable diagnosis support algorithm. A screen output control unit controls an output of an information display screen including an information display region in which diagnosis support information of the suitable diagnosis support algorithm is displayed.