CATH Lab Image Matching for Remote Guidance and Privacy Protection
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Solution Overview
Problem
Existing medical procedures lack effective systems for tracking and evaluating operator techniques, integrating imaging data for detailed outcome assessment, enabling remote clinician assistance, and securely sharing imaging data while protecting patient privacy.
Innovation Solution
A system that integrates computer vision and machine learning models to track medical instrument motion, assess lesion characteristics, and provide treatment strategies, while enabling secure communication and data sharing with remote clinicians, and generating de-identified imaging data for education and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If imaging data is shared with remote clinicians and used for training purposes, then clinician confidence and procedural outcomes are improved, but patient privacy and data security are compromised
Solution Approach 1:
The system extracts and removes personally identifiable information (PII) from imaging data through automated redaction processes. This extraction separates sensitive patient identifiers from the clinical imaging content, allowing the imaging data to be shared for remote consultation and training while protecting patient privacy.
Solution Approach 2:
The system introduces an intermediary processing layer that de-identifies imaging data before sharing. This intermediary process acts as a mediator between the original imaging data and the shared version, ensuring that patient privacy is protected while still enabling remote clinicians to access and utilize the imaging information for consultation and training purposes.
2Measurement precision
If multiple systems and data sources are integrated for comprehensive evaluation, then measurement precision and outcome assessment are improved, but device complexity increases
Solution Approach 1:
The system merges multiple data sources including imaging data, device tracking data, and procedural information into a single integrated platform. This consolidation allows comprehensive outcome assessment and precise measurement while managing complexity through unified data architecture and centralized processing.
Solution Approach 2:
The system is designed with multi-functional capabilities that can handle various types of data (imaging, tracking, procedural) and perform multiple functions (evaluation, training, remote consultation) within a single platform. This universality reduces the need for separate specialized systems and simplifies integration.
3Productivity
If real-time tracking and analysis are implemented during procedures, then productivity and decision-making speed are improved, but use of energy and computational resources increases
Solution Approach 1:
The system implements selective real-time tracking and analysis, focusing computational resources on critical parameters and key moments during the procedure. Rather than continuously analyzing all data at maximum intensity, the system applies partial processing to essential elements, maintaining procedural efficiency while managing computational energy consumption.
Data Source
AI summary
An example medical system includes a memory and processing circuitry communicatively coupled to the memory. The processing circuitry is configured to receive imaging data from one or more image sensors during a first cardiac catheterization medical procedure. The processing circuitry is configured to execute at least one computer vision model to identify a second cardiac catheterization medical procedure of a plurality of cardiac catheterization medical procedures stored in the memory. The processing circuitry is configured to output, for display, a representation of imaging data from the first cardiac catheterization medical procedure and a representation of imaging data from the second cardiac catheterization medical procedure.