Surgical Hub Resource Allocation for Adaptive Device Autonomy
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Solution Overview
Problem
Medical systems and facilities are slow to adopt newer surgical technologies due to patient safety concerns and a preference for traditional practices, limiting the implementation of advanced surgical devices and systems.
Innovation Solution
A surgical hub autonomously detects and coordinates surgical devices, determining which features to enable or disable based on available resources and surgical procedure requirements, optimizing resource allocation to improve surgical outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles are equipped with multiple sensors and complex computing hardware to improve sensing and decision-making capabilities, then the vehicle's intelligence and safety are enhanced, but the cost, power consumption, and computational burden increase significantly
Solution Approach 1:
The autonomous vehicle system is divided into multiple distributed computing units (edge computing devices) rather than relying on a single central processor. Each computing unit handles specific sensing and decision-making tasks, allowing parallel processing that reduces overall power consumption while maintaining high computational capacity for safety-critical functions.
Solution Approach 2:
A communication network acts as an intermediary between sensors, computing units, and actuators. This distributed architecture allows vehicles to share computational loads and sensor data through V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications, reducing the power consumption of individual vehicles while enhancing collective safety.
2Ease of manufacture
If traditional centralized control architecture is used to simplify system design, then implementation is easier, but communication delays and single points of failure reduce system reliability and real-time response capability
Solution Approach 1:
The centralized control architecture is segmented into distributed control units located at different vehicles and infrastructure points. Each unit operates autonomously within its local context while contributing to overall system coordination, eliminating single points of failure and reducing communication delays for critical decisions.
Solution Approach 2:
The control architecture transitions from static centralized authority to dynamic distributed coordination. Control responsibilities are dynamically assigned based on vehicle roles, sensor capabilities, and real-time conditions, allowing the system to adapt to changing circumstances and maintain reliability even when individual units fail.
3Measurement precision
If high-resolution sensors and frequent data transmission are deployed to improve perception accuracy, then sensing precision is enhanced, but data transmission bandwidth requirements and processing complexity increase
Solution Approach 1:
Critical processing functions are extracted from individual vehicles and distributed to edge computing devices at infrastructure locations. Sensors capture high-resolution data, but heavy processing and data fusion occur at distributed edge nodes, reducing onboard processing complexity while maintaining high sensing precision through collaborative computation.
Solution Approach 2:
The system processes data at varying levels of detail based on situational context. During normal operation, reduced-resolution data is processed to minimize bandwidth and computational requirements. When safety-critical situations are detected, the system activates full high-resolution processing only for relevant sensor streams and time windows, balancing precision with complexity management.
Data Source
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AI summary
Systems, methods, and instrumentalities are described herein for adapted autonomy functions and system interconnections. A surgical hub may detect a first surgical device in the surgical environment. The first surgical device may include a first plurality of features. The surgical hub may include a plurality of resources. The surgical hub may detect a second surgical device in the surgical environment. The second surgical device may include a second plurality of features. The surgical hub may determine one or more features from the first plurality of features for the first surgical device to perform and one or more features from the second plurality of features for the second surgical device to perform based on the plurality of resources.