Network System for Concurrent Medical Imagery Annotation
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Solution Overview
Problem
Current systems lack efficient methods for concurrent collaboration and annotation of streaming medical imagery data across heterogeneous networks, including live and archived data, which hinders real-time communication and knowledge sharing among diverse teams in medical settings.
Innovation Solution
A network system that enables concurrent collaboration by allowing multiple users to view and annotate streaming medical imagery data in real-time, using TIMS Clini-Pod Network Servers and Clini-Docks, which manage and synchronize data across various medical modalities, and encapsulate annotations and metadata into single file formats compliant with DICOM standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple users concurrently annotate and collaborate on streaming medical imagery data across heterogeneous networks, then real-time communication and knowledge sharing are enhanced, but system complexity and data synchronization challenges increase
Solution Approach 1:
The patent combines multiple collaboration functions (annotation, telestration, sketching, audio, video) and heterogeneous data sources (medical imagery, waveforms, audio, haptic signals, clinical documents) into a unified network system that processes and synchronizes all data streams concurrently through centralized server infrastructure
Solution Approach 2:
The network system is designed to handle multiple types of data (imagery, audio, video, haptic signals, clinical documents) and multiple collaboration modes (synchronous and asynchronous) through a single multi-functional platform that supports heterogeneous networks and various medical modalities
2Loss of information
If streaming medical imagery data is acquired from multiple network-connected devices and modalities, then data comprehensiveness and diagnostic capability are improved, but data management and synchronization difficulty increase
Solution Approach 1:
The patent introduces centralized server infrastructure and network systems that act as intermediaries between multiple imaging devices, sensors, and storage systems, managing data acquisition, standardization, and synchronization while reducing the complexity burden on individual devices
Solution Approach 2:
The system segments data management functions across multiple components including network-connected imaging devices, servers for data reception and processing, and storage systems, allowing each component to handle specific tasks independently while maintaining overall system coordination
3Ease of operation
If annotations and metadata are encapsulated and saved in single file formats compliant with DICOM standards, then data organization and retrieval efficiency are improved, but file format compatibility and processing complexity increase
Solution Approach 1:
The patent transforms diverse data formats (medical imagery, waveforms, audio, haptic signals, clinical documents) into a standardized single file format structure compliant with DICOM standards, changing the parameter of data organization from heterogeneous multiple files to unified structured files with standardized metadata encapsulation
Data Source
AI summary
The invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. These methods can leverage domain knowledge and clinical expertise with cognitive collaboration, augmented medical intelligence and cybernetic workflow streams for learning health care systems. The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms. It provides for both synchronous and asynchronous cognitive collaboration with multichannel, multiplexed imagery data streams during various stages of medical disease and injury management—detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs. The invention enables cognitive curation, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized medical intelligence.