Deep Learning CTR Assessment System for Chest X-Ray Analysis
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Solution Overview
Problem
Current methods for assessing cardiothoracic ratio (CTR) are manual, subjective, and lack real-time calculation capabilities, leading to variability in accuracy among physicians.
Innovation Solution
A method and system utilizing deep learning neural networks to classify and measure chest X-ray images, automatically extracting diameters of the thoracic cavity and cardiac silhouette, and calculating CTR in real-time, reducing subjective interpretation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement by physicians is used to calculate CTR, then the assessment can be performed with simple equipment, but the accuracy varies due to subjective interpretation and lacks real-time capability
Solution Approach 1:
The patent replaces the manual mechanical measurement process with an automated deep learning neural network system. The neural network automatically detects and measures the cardiac silhouette and thoracic cavity dimensions from chest X-ray images, eliminating subjective human interpretation and providing consistent, accurate measurements. This substitution of manual mechanical measurement with automated intelligent systems directly resolves the contradiction between measurement precision and extent of automation.
2Reliability
If multiple deep learning neural network classifiers are used to classify and process X-ray images, then the accuracy and reliability of CTR assessment is improved, but the device complexity increases
Solution Approach 1:
The patent divides the CTR assessment process into multiple specialized neural network classifiers, each responsible for a specific task: one classifier identifies whether the image is a chest X-ray, another determines the view type (PA or AP), and a third performs the actual CTR measurement. This segmentation of functionality into specialized modules improves reliability through task-specific optimization while making the overall system more manageable and interpretable.
Solution Approach 2:
The deep learning neural network system is designed to handle multiple functions within a unified framework: image classification, view identification, and CTR measurement. The system can process both PA and AP chest X-ray views and automatically adapts to determine the appropriate measurement approach, providing universal functionality that improves reliability across different input scenarios.
3Productivity
If automated deep learning system is implemented for real-time CTR calculation, then productivity and consistency are improved, but the initial system development and implementation complexity increases
Solution Approach 1:
The system performs preliminary classification of the input image to determine whether it is a chest X-ray and what view type it represents before proceeding to CTR measurement. This preliminary action ensures that the appropriate measurement protocols are applied and prevents erroneous processing of non-chest images, improving productivity by avoiding rework and enhancing reliability through proper preprocessing.
Solution Approach 2:
The deep learning neural network system is designed to automatically perform all CTR assessment tasks without requiring manual intervention. The system self-regulates by identifying image types, selecting appropriate processing pathways, and generating measurements autonomously. This self-service capability maximizes productivity while the modular architecture keeps implementation complexity manageable.
Data Source
AI summary
A method for assessing cardiothoracic ratio (CTR) includes following steps. A testing X-ray image database of a subject is provided. A first image data classifying step is performed, wherein the testing X-ray image database is classified by a first deep learning neural network classifier to obtain a testing chest X-ray image data. A second image data classifying step is performed, wherein the testing chest X-ray image data is classified by a second deep learning neural network classifier to obtain a target chest X-ray image data. A feature extracting step is performed, wherein a diameter of thoracic cavity and a diameter of cardiac silhouette of the target chest X-ray image data are captured automatically and then trained to achieve a convergence by a third deep learning neural network classifier. An assessing step is performed, wherein an assessing result of CTR is obtained according to a feature of CTR.