Imaging Data Set Parameter Indication for Medical Device Placement
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Solution Overview
Problem
Tomographic imaging systems face challenges in efficiently analyzing large datasets for medical devices and biological lumens, leading to mental fatigue and potential observer errors due to the time-consuming process of scrolling through images and discerning proper vs. improper device placement.
Innovation Solution
The system employs enhanced graphical displays with visual, audio, or tactile alerts to emphasize important parameters in imaging data sets, using a CPU and storage to analyze data, assign indicators, and provide alerts when parameters exceed threshold values, aiding healthcare providers in quickly synthesizing data and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a clinician manually scrolls through image frames to analyze tomographic imaging data, then detailed evaluation of medical device placement and anatomical structures can be performed, but the process is time-consuming and leads to mental fatigue and observer errors
Solution Approach 1:
The system segments the continuous image data set into discrete frames and identifies key parameters (stent apposition, vessel dimensions, plaque characteristics) that can be independently evaluated. This segmentation allows the system to process and highlight critical information from each frame without requiring manual review of the entire data set, reducing review time while maintaining evaluation accuracy.
Solution Approach 2:
The system introduces an intermediary computational algorithm that acts as a mediator between the raw image data and the clinician's evaluation. This algorithm automatically detects medical devices, measures anatomical parameters, and generates visual indicators that assist the clinician in assessing device placement and anatomical structures, thereby reducing both time and mental effort required for accurate evaluation.
2Reliability
If a clinician manually evaluates large data sets of tomographic images, then comprehensive analysis can be performed, but extended periods of scrutiny lead to mental fatigue and observer error
Solution Approach 1:
The system enables self-service evaluation by automatically performing measurements, detecting devices, and generating visual indicators without requiring continuous manual intervention. The computational algorithm independently processes the image data set, identifies critical parameters, and presents them in an easily interpretable format, reducing the operator burden while maintaining or improving evaluation reliability.
Solution Approach 2:
The system provides immediate visual feedback through indicators that highlight areas of concern such as incomplete stent apposition, vessel stenosis, or plaque characteristics. This feedback mechanism allows clinicians to quickly identify and focus on critical findings without having to mentally process the entire data set, thereby reducing mental fatigue while maintaining high evaluation reliability.
3Loss of information
If standard image display is used to show medical device placement, then the complete image data set can be displayed, but it is difficult to immediately discern problematic placement due to visual similarity between proper and improper placement
Solution Approach 1:
The system uses color changes and visual indicators to differentiate between proper and improper device placement. For example, different colors or highlighting patterns are applied to indicate areas of incomplete apposition versus proper apposition, making it immediately discernible to the clinician without losing the underlying anatomical context and device positioning information.
Solution Approach 2:
The system adds another dimension of information display by overlaying visual indicators and measurements directly on the image frames. This dimensional enhancement provides additional spatial and quantitative context that helps clinicians immediately detect problematic device placement while maintaining the complete anatomical information from the original images.
Data Source
AI summary
Systems and methods for aiding users in viewing, assessing and analyzing images, especially images of lumens and medical devices contained within the lumens. Systems and methods for interacting with images of lumens and medical devices, for example through a graphical user interface.