Automated Acute Aortic Syndrome Identification in CT Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic imaging methods for acute aortic syndromes in computed tomography (CT) images face challenges such as false positives and delayed reporting due to the need for expert interpretation and the time-consuming process of image processing and verification, especially during on-call hours when specialists may not be immediately available.
Innovation Solution
An automated system that uses a processor and non-transitory computer-readable medium to analyze CT images of the ascending, aortic arch, and descending aorta, determining variations in radiodensity values like Hounsfield Units (HU) to calculate a likelihood of acute aortic syndrome, providing immediate results without expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert interpretation of CT images is used to diagnose acute aortic syndromes, then diagnostic accuracy is improved, but turnaround time increases and false positives occur due to uncertainty and motion artifacts
Solution Approach 1:
An automated analysis system serves as an intermediary between CT image acquisition and expert radiologist interpretation. The system processes CT images to generate preliminary diagnostic reports with confidence scores, providing a bridge that reduces turnaround time while maintaining diagnostic accuracy through automated feature extraction and analysis algorithms
Solution Approach 2:
The system enables self-service diagnosis by automatically analyzing CT images without requiring immediate expert intervention. The automated algorithm independently performs image processing, feature extraction, and diagnostic assessment, allowing the system to serve itself in the initial diagnostic phase and reducing dependency on immediate expert availability
2Reliability
If expert interpretation is performed during on-call hours, then diagnostic capability is maintained, but reporting is delayed due to unavailability of specialists
Solution Approach 1:
The automated system provides self-service diagnostic capability that operates independently of expert availability. During on-call hours when specialists are unavailable, the system autonomously performs image analysis and generates diagnostic reports, maintaining reliable diagnostic capability without reporting delays
Solution Approach 2:
The system performs preliminary diagnostic action automatically before expert review is available. By conducting initial image analysis and generating preliminary reports during on-call hours, the system prepares diagnostic information in advance, eliminating waiting time while experts become available
3Productivity
If automated analysis based on radiodensity variation is used, then turnaround time is reduced and immediate results are provided, but diagnostic precision may be compromised compared to expert interpretation
Solution Approach 1:
The system implements feedback mechanisms where automated analysis results are compared with expert interpretations to continuously refine algorithms. Confidence scores generated by the automated system provide feedback on diagnostic certainty, allowing the system to maintain high precision while achieving rapid turnaround by processing cases with high confidence automatically
Data Source
AI summary
Systems and methods are provided for automated identification of acute aortic syndromes in computed tomography images. A region of interest in a chest of a patient is imaged via a computed tomography (CT) scanner to provide images at a plurality of locations. The region of interest includes one of an ascending aorta, an aortic arch, and a descending aorta of the patient. For each of the plurality of locations within the region of interest, a value representing a variation in radiodensity values within the location is determined from the image to provide a set of variation values. A parameter representing a likelihood that the patient is experiencing an acute aortic syndrome is determined via a derived model from the set of variation values. The parameter representing the likelihood that the patient is experiencing the acute aortic syndrome is provided to a user at an associated output device.