Predicting Cerebral Cortical Contraction from CT Images
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Solution Overview
Problem
Current methods for diagnosing Alzheimer's disease rely on PET-CT devices, which provide a CT image but do not utilize it for diagnosis, and require separate MRI devices to obtain region-specific cerebral cortical contraction rates.
Innovation Solution
An apparatus using deep learning and machine learning to predict region-specific cerebral cortical contraction rates from CT images, by training a deep learning network to segment CT images and extract semantic features, and then using a machine learning model to predict contraction rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a PET-CT device is used to obtain CT images for Alzheimer's disease diagnosis, then the CT image is obtained, but the CT image cannot provide region-specific cerebral cortical contraction rate information
Solution Approach 1:
The patent makes the CT image serve multiple functions: it is used both for its original diagnostic purpose and for predicting region-specific cerebral cortical contraction rates through deep learning and machine learning models. This eliminates the need for separate MRI devices by enabling the CT image to provide additional diagnostic information that was previously only available from MRI.
Solution Approach 2:
The patent introduces deep learning networks and machine learning models as intermediaries that process CT images to extract and predict region-specific cerebral cortical contraction rates. These computational models act as mediators that transform the CT image data into the required diagnostic information without requiring additional imaging hardware.
2Loss of information
If separate MRI device is used to obtain region-specific cerebral cortical contraction rate, then the information is available, but the diagnostic process becomes more complex and time-consuming
Solution Approach 1:
The patent combines the acquisition of CT images and the prediction of region-specific cerebral cortical contraction rates into a single integrated process. By using deep learning and machine learning models that process CT image data, the system merges what were previously separate diagnostic steps (CT imaging and MRI-based contraction rate measurement) into one unified workflow, reducing diagnostic time and complexity.
3Device complexity
If CT image is used for Alzheimer's disease diagnosis, then the imaging process is simplified, but the CT image lacks the necessary information for accurate diagnosis
Solution Approach 1:
The patent replaces the mechanical/imaging system (MRI device) with a computational system (deep learning and machine learning models). Instead of using physical MRI equipment to obtain contraction rate information, the system uses computational algorithms to predict this information from CT images, substituting mechanical imaging with intelligent data processing.
Solution Approach 2:
The patent transforms the CT image data through deep learning feature extraction and machine learning prediction to generate new diagnostic parameters (region-specific cerebral cortical contraction rates) that were not directly visible in the original CT images. This parameter transformation enables accurate diagnosis using only CT imaging.
Data Source
AI summary
The present invention relates to an apparatus for predicting a region-specific cerebral cortical contraction rate on the basis of a CT image. The present invention may comprise: a deep learning step of a deep learning network learning, by selecting and using CT images of a plurality of patients and segmentation information thereof, a correlation between the CT images and the segmentation information; a feature extraction step of extracting, on the basis of each piece of the segmentation information, semantic feature information corresponding to the CT images; a machine learning step of a machine learning model learning, after a plurality of region-specific cerebral cortical contraction rates corresponding to each piece of the semantic feature information are additionally acquired, a correlation between the semantic feature information and the region-specific cerebral cortical contraction rates; a segmentation step of, when an image to be analyzed is input, acquiring segmentation information corresponding to the image to be analyzed, through the deep learning network; and a prediction step of predicting and reporting, after semantic feature information corresponding to the image to be analyzed is extracted on the basis of the segmentation information, a region-specific cerebral cortical contraction rate corresponding to the semantic feature information through the machine learning model.