Automated LVO Detection Using CTA Contrast Density Analysis
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Solution Overview
Problem
Current systems face challenges in rapidly and accurately diagnosing large vessel occlusions (LVO) in acute ischemic stroke patients at primary stroke centers (PSCs), leading to delayed transfers to comprehensive stroke centers (CSCs, which can result in wasted time and potential loss of beneficial treatment opportunities due to the lack of trained personnel and limitations in existing automated ASPECTS reading technologies.
Innovation Solution
A method that assesses relative flows of contrast agent through computed tomography angiogram (CTA) images by measuring contrast agent density, overlaying vascular regions, calculating affected tissue areas, and using multi-phase protocols to determine the presence of LVO, which includes interpolating density values, calculating volumes, and correlating with past data to improve diagnosis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automated ASPECTS reading technology is used at PSCs, then diagnostic speed is improved, but diagnostic accuracy deteriorates due to lack of trained personnel and technology limitations
Solution Approach 1:
The patent introduces an automated LVO detection system that acts as an intermediary between the CT imaging data and the final diagnosis. This system processes the imaging data through algorithms that identify characteristic patterns of large vessel occlusion, providing a bridge that enables accurate LVO detection without requiring specialized human expertise at the PSC level.
Solution Approach 2:
The patent replaces the manual mechanical process of expert radiologist review with an automated computational system. The system uses image processing algorithms and pattern recognition to substitute for the human expert's visual analysis, thereby maintaining diagnostic accuracy while enabling rapid assessment at facilities without specialized personnel.
2Measurement precision
If patient transfer to CSC is delayed for further diagnostic evaluation, then diagnostic accuracy is improved, but treatment time is worsened due to time-sensitive nature of stroke treatment
Solution Approach 1:
The patent performs preliminary LVO detection and assessment at the PSC before patient transfer to the CSC. By conducting the diagnostic evaluation in advance during the transfer decision-making process, the system eliminates the need for delayed diagnostic assessment at the receiving facility, thereby maintaining diagnostic accuracy while minimizing treatment time delays.
Solution Approach 2:
The patent segments the diagnostic process into distinct phases: initial LVO detection at the PSC using automated analysis, transfer decision-making based on those results, and subsequent comprehensive evaluation at the CSC. This segmentation allows critical LVO identification to occur early at the community level, separating time-sensitive detection from less time-sensitive comprehensive assessment.
3Adaptability or versatility
If comprehensive stroke care facilities are located far from PSCs, then treatment capabilities are improved, but transfer time is worsened affecting treatment outcomes
Solution Approach 1:
The patent enables PSCs to perform self-service LVO detection and diagnostic evaluation using the automated system. By empowering community hospitals with the capability to independently identify LVO patients and make informed transfer decisions, the system reduces dependence on immediate proximity to CSCs, thereby mitigating the impact of distance on treatment timing while preserving access to comprehensive care capabilities.
Data Source
AI summary
The invention relates to systems and methods to assist physicians in decision making for stroke patients. In particular the systems and methods can be used to assist physicians to decide on whether a patient with an acute ischemic stroke has a large vessel occlusion (LVO) and should be transferred from a community hospital to a larger hospital to undergo an endovascular thrombectomy procedure.