Deep Learning Aortic CT Analysis for Type A/B Dissection Classification
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Solution Overview
Problem
Accurate determination of aortic dissection from CT images relies on professional physician experience, leading to delays in treatment and increased risk of fatal outcomes.
Innovation Solution
A method and system utilizing deep learning models for part detection and status analysis of aortic CT images to automatically classify type A and type B aortic dissection, enabling rapid diagnosis and treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional physicians manually analyze CT images to determine aortic dissection, then diagnostic accuracy is improved, but treatment time is increased
Solution Approach 1:
The patent replaces the mechanical system of manual physician analysis with an automated deep learning-based image analysis system. The system uses neural networks to automatically detect aortic dissection in CT images, eliminating the need for manual review while maintaining diagnostic accuracy. This substitution of automated computational analysis for human manual analysis resolves the contradiction by providing both high accuracy and rapid results.
2Loss of time
If automated image analysis is implemented, then treatment time is reduced, but diagnostic reliability may be worsened
Solution Approach 1:
The patent implements feedback mechanisms where the automated analysis system continuously learns from and refines its predictions based on established medical criteria and validation against known cases. The system incorporates feedback loops that allow it to adjust its analysis based on the detected features and comparison with reference data, ensuring that automated analysis maintains high diagnostic reliability while providing rapid results.
Solution Approach 2:
The patent introduces an intermediary validation layer that acts as a mediator between automated detection and final diagnosis. This intermediary component verifies automated analysis results against multiple criteria and thresholds before confirming the diagnosis, ensuring that the automated system maintains high reliability. The intermediary serves as a safeguard that preserves diagnostic accuracy while enabling rapid automated processing.
3Productivity
If deep learning models are used for automated analysis, then productivity is improved, but device complexity is increased
Solution Approach 1:
The patent segments the complex deep learning analysis system into distinct functional modules: image preprocessing module, feature extraction module, classification module, and result validation module. Each module performs a specific function and can be independently optimized or replaced. This segmentation manages the inherent complexity by organizing it into manageable, well-defined components while maintaining high analysis productivity.
Data Source
AI summary
A method for analyzing aortic CT images includes: receiving multiple original CT images and selecting a sequence of chest CT images therefrom; generating, using a part detection model, for each of the chest CT images, a detection result that indicates whether the chest CT image represents an ascending aorta; generating, using a status analysis model, for each of the chest CT images, an analysis result that indicates whether the chest CT image shows aortic dissection; and when determining that at least N chest CT image(s) from consecutive M number of the chest CT images show aortic dissection, determining whether the detection result of at least one of the at least N chest CT image(s) represents an ascending aorta, and if affirmative, generating a type A aortic dissection result or otherwise, generating a type B aortic dissection result.