Brain Age Prediction Model for Objective MRI Analysis
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Solution Overview
Problem
Current methods for interpreting brain magnetic resonance images are subjective and lack reproducibility, leading to varying diagnostic results among clinicians and an inability to provide a quantitative indicator for brain degeneration.
Innovation Solution
A model training method that uses a brain age prediction model, established through a training set of healthy brain images, validated with a set of healthy images, and tested with unhealthy images, to automatically and systematically evaluate overall brain changes, incorporating machine learning algorithms and structural covariance networks from MRI images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a clinician performs clinical interpretation based on medical training and experience, then diagnostic interpretation can be provided, but the results vary among different clinicians and lack reproducibility
Solution Approach 1:
The patent replaces the mechanical system of human clinical interpretation with an automated computer-based image processing system. The system uses algorithms to automatically analyze brain MRI images, extract features, and generate diagnostic reports, eliminating the variability inherent in human interpretation while maintaining ease of use through automated processing.
Solution Approach 2:
The patent transforms subjective clinical interpretation into objective quantitative parameters by extracting measurable features from brain MRI images. The system converts visual assessment into numerical data such as brain age predictions, structural covariance metrics, and degeneration indicators, providing reproducible quantitative results that can be consistently measured and compared.
2Reliability
If automated image processing is implemented, then reproducibility and objectivity are improved, but the complexity of the system increases
Solution Approach 1:
The patent divides the complex model training system into distinct modular components: data preprocessing module, feature extraction module, model training module, validation module, and prediction module. Each module performs a specific function and can be independently developed and tested, reducing the overall system complexity while maintaining objective and reproducible results.
3Measurement precision
If machine learning algorithms are used to predict brain age, then quantitative indicators for brain degeneration are provided, but the training process requires extensive data processing
Solution Approach 1:
The patent performs preliminary actions by pre-processing and pre-processing brain MRI images before model training, including normalization, segmentation, and feature extraction. The system also performs preliminary model validation using cross-validation techniques to optimize hyperparameters before final training, reducing the time required for the complete training process while maintaining precise quantitative measurements of brain degeneration.
Data Source
AI summary
A model training method and an electronic device are provided. The method includes the following steps: establishing a brain age prediction model according to a training set; adjusting a parameter in the brain age prediction model according to a validation set; inputting a test set into the brain age prediction model with the adjusted parameter to obtain a plurality of first predicted brain ages; determining whether the first predicted brain ages satisfy a first specific condition; and completing training of the brain age prediction model when the first predicted brain ages satisfy the first specific condition.