Edge AI Telesurgery Computing for Low-Latency Robotic Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional surgical methods face high communication latency and bandwidth issues during robotic telesurgery, leading to surgical inaccuracies and patient safety risks due to delays in auditory, visual, and tactile feedback.
Innovation Solution
Implementing edge computing and artificial intelligence in robotic telesurgery systems, which use sensors to measure speed and capture images, and an AI accelerator to predict adverse events, allowing the surgical robot to perform actions independently of network conditions, thereby reducing reliance on centralized processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized cloud processing is used for robotic telesurgery, then comprehensive data analysis capability is improved, but communication latency and bandwidth requirements worsen
Solution Approach 1:
The system segments processing functions by deploying edge computing nodes at multiple hierarchical levels (surgical robot, hospital, regional data center). Each level handles specific processing tasks locally, dividing the centralized cloud processing into distributed edge processing units that operate autonomously to reduce communication latency.
Solution Approach 2:
Edge computing nodes serve as intermediaries between the surgical robot and centralized cloud systems. These intermediary devices process data locally and only transmit essential information to the cloud, reducing bandwidth requirements and communication latency while maintaining comprehensive data analysis capability.
2Loss of information
If real-time feedback is transmitted through centralized networks, then complete information availability is improved, but network bandwidth consumption worsens
Solution Approach 1:
The system extracts and processes critical information locally at edge computing nodes, separating essential real-time data from comprehensive data sets. Only necessary information is transmitted through the network, reducing bandwidth consumption while ensuring complete availability of critical surgical information.
Solution Approach 2:
Different levels of the distributed system handle different qualities of data processing. Local edge nodes provide real-time feedback with high information quality for immediate surgical decisions, while comprehensive data analysis is performed at higher hierarchical levels, optimizing bandwidth usage across the network.
3Productivity
If autonomous robotic action is enabled, then surgical speed is improved, but system complexity worsens
Solution Approach 1:
The system performs preliminary actions by pre-processing data and preparing surgical plans at edge computing nodes before actual surgical execution. Machine learning models are pre-trained and deployed to edge devices, enabling autonomous robotic actions without requiring complex real-time centralized processing during surgery.
Solution Approach 2:
The surgical robot performs self-service through autonomous decision-making capabilities enabled by edge computing and machine learning. The system independently processes sensor data, identifies surgical targets, and executes surgical actions without continuous human intervention or complex centralized control, reducing operational complexity while maintaining high surgical speed.
Data Source
AI summary
Methods, apparatuses, and systems for edge computing for robotic telesurgery using artificial intelligence are disclosed. A robotic surgical system includes surgery equipment and can communicate via a cloud network. The system can include operation room (OR) equipment and a surgical computer. The surgical computer transfers data between a remote surgeon, the OR equipment, and the surgery equipment. The surgical computer receives, using the OR equipment and the surgery equipment, data related to a surgical procedure. The data is related to the surgical procedure and is computed using artificial intelligence (AI). The surgical computer determines a risk assessment based on data related to the surgery equipment. The surgical computer sends an indication of the determined risk assessment to the remote surgeon.