Deep Learning Neural Network for Aortic Aneurysm Detection
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Solution Overview
Problem
The accuracy of computer-based systems for detecting aortic aneurysms is limited by the variability of clinical data, leading to inefficient manual analysis and limited applicability to individual patients, with existing approaches failing to effectively automate the detection and prioritization of high-risk patients.
Innovation Solution
A predictive aortic aneurysm detection system utilizing a deep learning neural network that combines ensemble objection detection models (YOLO and Faster RCNN) with segmentation models (U-Net) for automated analysis of CT scans, enabling efficient detection and segmentation of aortic aneurysms, and prioritization of patients based on risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual analysis methods are used for aortic aneurysm detection, then individualized clinical context can be considered, but the productivity and efficiency of detection are limited
Solution Approach 1:
The system enables self-service by automatically performing detection, segmentation, and risk stratification without requiring manual clinical analysis. The neural network autonomously processes CT scans and clinical data to generate diagnostic outputs, eliminating the need for radiologists to manually evaluate each case while maintaining high accuracy through automated feature extraction and pattern recognition
Solution Approach 2:
The patent replaces the mechanical manual analysis system with an automated computational system. Instead of radiologists manually reviewing CT scans and clinical data, the system uses neural networks and machine learning algorithms to automatically detect aortic aneurysms, segment vessels, and assess rupture risk, thereby dramatically improving productivity while preserving individualized assessment capabilities
2Productivity
If computer-based detection systems are implemented, then productivity and efficiency are improved, but the accuracy is limited by variability of clinical data
Solution Approach 1:
The system merges multiple data sources including CT scan images, clinical patient data, and risk factors into a unified analytical framework. By combining these diverse inputs, the neural network compensates for variability in individual data sources and achieves more accurate and reliable detection results than any single input could provide alone
Solution Approach 2:
The system performs preliminary processing and standardization of clinical data before analysis. CT scans undergo preprocessing to normalize variations in imaging protocols, and clinical data is structured to accommodate different formats and sources. This preliminary action ensures that variability in input data does not compromise the accuracy of subsequent detection and analysis
3Loss of time
If automated detection systems are used, then time consumption is reduced, but the complexity of the system increases
Solution Approach 1:
The system segments the detection process into distinct functional modules: CT scan preprocessing, aortic vessel segmentation, aneurysm detection, risk factor integration, and outcome prediction. Each module is handled by specialized neural network components that process specific aspects of the data independently, reducing overall system complexity while enabling parallel processing to minimize time loss
Data Source
AI summary
Systems and methods for detecting aortic aneurysms using ensemble based deep learning techniques that utilize numerous computed tomography (CT) scans collected from numerous de-identified patients in a database. The system includes software that automates the analysis of a series of CT scans as input (in DICOM file format) and provides output in two dimensions: (1) ranking CT scans by risks of adverse events from aortic aneurysm, (2) providing aortic aneurysm size estimates. A repository of CT scans may be used for training of deep neural networks and additional data may be drawn from localized patient information from institutions and hospitals which grant permission.