MRI Trained Model for Dementia Progression Prediction
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Solution Overview
Problem
Existing technologies lack the capability to predict the degree of progression of dementia, which is crucial for supporting early diagnosis and selecting appropriate treatment methods.
Innovation Solution
A non-transitory computer-readable storage medium storing a program that includes a trained model for predicting the degree of dementia progression using MRI images and functional change information, employing machine learning techniques such as SVM, DNN, CNN, and RNN to analyze brain atrophy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning methods are used to predict dementia progression, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
A trained machine learning model acts as an intermediary between MRI image data and diagnostic conclusions. The model processes complex image analysis tasks internally, providing simplified prediction outputs (dementia progression degree) to clinicians without requiring them to understand the underlying complex algorithms
Solution Approach 2:
The patent extracts and isolates the complex machine learning inference process into a separate trained model component. This allows the core diagnostic functionality to be improved through advanced algorithms while keeping the overall system architecture manageable by separating the complex processing from the user interface and data acquisition layers
2Measurement precision
If multiple machine learning algorithms are employed, then prediction accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
Multiple machine learning algorithms (SVM, DNN, CNN, RNN) are merged into a single integrated trained model. The system presents a unified interface that automatically selects and combines these algorithms, eliminating the need for operators to manually configure or switch between different complex algorithms while maintaining high prediction accuracy
3Measurement precision
If brain atrophy analysis is performed over time, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis by automatically selecting relevant time points and preprocessing MRI images before detailed atrophy measurement. This preliminary action reduces the computational burden on subsequent processing steps, enabling long-term longitudinal analysis without proportionally increasing total processing time
Solution Approach 2:
The patent implements optimized processing that skips redundant analysis steps. By using the trained model to quickly evaluate and filter less relevant temporal data, the system rushes through routine processing stages while maintaining comprehensive long-term analysis, thereby reducing overall processing time while preserving measurement precision
Data Source
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AI summary
There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.