Machine Learning Model for Aortic Aneurysm Diagnosis
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Solution Overview
Problem
Current methods for diagnosing and managing abdominal aortic aneurysms (AAAs) and endoleaks after endovascular aneurysm repair (EVAR) are limited by inaccuracies in imaging and variability in clinical interpretation, leading to inconsistent monitoring and potential missed ruptures.
Innovation Solution
A computer-implemented method using computed tomography angiography (CTA) images and a trained machine learning model to classify properties of AAAs, including the presence and severity of endoleaks, allowing for precise diagnosis, prognosis, and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of CTA images is used for diagnosing AAAs and endoleaks, then clinical flexibility is maintained, but measurement precision and diagnostic accuracy deteriorate due to variability in interpretation
Solution Approach 1:
A machine learning model serves as an intermediary between the CTA images and the final diagnosis, automatically extracting features and classifying endoleak presence and type. This intermediary process standardizes the interpretation across different cases and clinicians, eliminating human variability while maintaining clinical workflow integration.
Solution Approach 2:
The manual mechanical process of visual inspection and interpretation by clinicians is replaced with an automated computational system that processes CTA images through trained machine learning algorithms. This substitution provides consistent, reproducible measurements and classifications without human fatigue or variability.
2Reliability
If automated machine learning analysis is implemented for CTA images, then measurement precision and consistency improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The machine learning model performs multiple diagnostic functions simultaneously: detecting endoleak presence, classifying endoleak types (I-V), measuring aneurysm sac volume, and identifying relevant anatomical structures. This multi-functionality consolidates what would otherwise require multiple separate tools or manual processes into a single integrated system.
Solution Approach 2:
The system automatically processes CTA images without requiring manual annotation or intervention during the diagnostic process. The machine learning model self-adjusts based on trained data and provides consistent classifications, reducing the need for repeated manual adjustments or recalibrations by operators.
3Loss of information
If detailed classification of endoleak properties is performed, then diagnostic information completeness improves, but analysis time and processing complexity increase
Solution Approach 1:
The machine learning model is pre-trained on extensive datasets of labeled CTA images with known endoleak characteristics. This preliminary training allows the model to rapidly classify new images without requiring time-consuming manual analysis, as the classification rules and feature weights have already been optimized during the training phase.
Solution Approach 2:
The diagnostic analysis is segmented into distinct classification categories (endoleak presence, type classification I-V, aneurysm sac volume measurement). This segmentation allows the system to process different aspects of the diagnosis in parallel or hierarchical fashion, providing comprehensive information without requiring sequential analysis of all parameters.
Data Source
AI summary
Techniques for diagnosing a patient having an AAA. The techniques include using a computer hardware processor to perform: accessing computed tomography angiography (CTA) images of a portion of the patient, the portion of the patient including the AAA of the patient; providing the CTA images as input to a trained machine learning model, the trained machine learning model being configured to classify a property of the AAA based on the CTA images; and determining, based on the classified property of the AAA, a diagnosis of the patient, the diagnosis including information identifying at least one condition (e.g., an endoleak, an AAA rupture) of the patient.