Multi-Input/Output Surgical Video Routing for Low-Latency AI
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Solution Overview
Problem
Existing surgical video processing systems are limited by constraints in video processing and transmission, which hinder the quality and quantity of video that can be subjected to artificial intelligence processing.
Innovation Solution
A surgical video system utilizing GPU-based artificial intelligence platforms for distributed processing, combined with a central switch and smart display, enables ultra-high-definition video transmission with low latency and flexible routing, allowing for real-time analysis and overlay combination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video is transmitted at ultra-high-definition quality, then video quality is improved, but transmission latency increases and processing capacity is overwhelmed
Solution Approach 1:
The system segments video processing across multiple GPU-based AI platforms, distributing the computational load to process ultra-high-definition video streams in parallel. This segmentation enables maintenance of high video quality while reducing processing latency through concurrent operation of multiple processing units.
Solution Approach 2:
A central switch acts as an intermediary device that receives ultra-high-definition video streams and routes them to appropriate AI platforms for processing. The switch manages data flow efficiently, enabling high-quality video transmission with minimized latency by optimizing the path between video sources, AI platforms, and displays.
2Productivity
If multiple video streams are processed simultaneously, then processing capacity is improved, but system complexity increases
Solution Approach 1:
The GPU-based AI platforms are designed with multi-functionality to handle various video processing tasks simultaneously, including multiple ultra-high-definition video streams, different AI algorithms, and diverse output requirements. This universality allows the system to increase processing capacity without proportionally increasing system complexity, as the same hardware infrastructure performs multiple functions.
Solution Approach 2:
The system incorporates feedback mechanisms where the central switch and AI platforms continuously monitor system load, video stream characteristics, and processing performance. This feedback enables dynamic resource allocation and load balancing across multiple platforms, allowing the system to handle increased processing capacity while managing complexity through adaptive control.
3Manufacturing precision
If video transmission bandwidth is increased, then video quality is improved, but system cost and infrastructure requirements increase
Solution Approach 1:
The system transitions from processing constraints in the time domain to parallel processing in the spatial domain by deploying multiple GPU-based AI platforms. This dimensional change allows ultra-high-definition video to be processed with adequate bandwidth by distributing the load across multiple processing nodes, reducing the bandwidth requirement per individual connection while maintaining overall video quality.
Data Source
AI summary
A surgical video system having: at least one video source; at least one artificial intelligence platform, the at least one artificial intelligence platform having at least one artificial intelligence solution for processing video received from the at least one video source and providing at least one output; a switch coupling the at least one video source to the at least one artificial intelligence platform; and a display coupled to the switch and the artificial intelligence platform, the display further comprising a processor and at least one user input device. The display is configured to control the at least one artificial intelligence solution and to display video from the at least one video source along with output from the artificial intelligence platform.