Computer-Aided Aneurysm Triage System Using Deep Learning
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Solution Overview
Problem
Current medical triage workflows are time-consuming, especially in emergency settings, leading to delayed identification and treatment of aneurysms, which can be difficult to detect due to their small size and ambiguous treatment options, potentially resulting in dangerous or fatal consequences.
Innovation Solution
A computer-aided aneurysm triage system that processes imaging data to rapidly identify suspected aneurysms, determine appropriate treatment options, and facilitate timely specialist notification, integrating with existing radiology workflows to reduce detection time and false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a standard radiology workflow is used for aneurysm detection, then the process includes multiple review steps (radiologist review, emergency department doctor review), but the time to identify and treat aneurysms is significantly increased
Solution Approach 1:
The system performs preliminary action by automatically detecting and flagging potential aneurysms before they reach the radiologist's queue. The deep learning model analyzes imaging data in advance, creating a pre-sorted queue where critical cases are prioritized and ready for rapid review and treatment.
Solution Approach 2:
The deep learning model acts as an intermediary between the imaging data and the radiologist's review process. It processes the imaging data, identifies potential aneurysms, and provides recommendations that guide the radiologist's review, thereby reducing the time required for manual analysis while maintaining detection accuracy.
2Measurement precision
If manual review by radiologists is used, then comprehensive analysis can be performed, but small aneurysms are often missed or delayed in detection
Solution Approach 1:
The system replaces the manual mechanical review process with an automated deep learning model that processes imaging data. This substitution enables the system to detect small aneurysms that may be overlooked by human reviewers, improving both detection precision and productivity by continuously scanning without fatigue.
Solution Approach 2:
The deep learning model changes the parameters of analysis by using advanced algorithms that can detect subtle patterns and small structures in imaging data. This allows for the detection of small aneurysms that require different analytical parameters compared to conventional manual review methods.
3Ease of operation
If conventional radiology workflow is used, then treatment decisions can be made, but the process is ambiguous and subjective leading to delayed treatment
Solution Approach 1:
The system implements feedback by providing real-time recommendations from the deep learning model to the radiologist and emergency department doctors. This feedback loop includes automated flagging of critical cases, suggested next steps for treatment, and prioritization recommendations, making the treatment decision process more straightforward and timely.
Solution Approach 2:
The system provides multi-functionality by handling multiple tasks within the workflow: automatic imaging data processing, aneurysm detection, prioritization of critical cases, and treatment recommendation generation. This universal approach streamlines the entire process from imaging to treatment decision, reducing time loss.
Data Source
AI summary
A system for computer-aided triage includes and/or interfaces with a computing system. A method for computer-aided triage includes receiving a data packet including a set of images; and processing the set of images to determine a suspected condition and/or associated features. Additionally or alternatively, the method can include any or all of: preprocessing the set of images; triggering an action based on the suspected condition and/or associated features; determining a recipient based on the suspected condition; preparing a data packet for transfer; transmitting information to a device associated with the recipient; receiving an input from the recipient and triggering an action based on the input; aggregating data; and/or any other suitable processes.