Robotic Surgery Navigation Using AI and Capacitive Hover Sensing
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Solution Overview
Problem
Existing robotic surgical systems lack advanced navigation, spatial recognition, and collision avoidance capabilities, particularly in identifying vascular or nervous structures and organs, which can lead to collisions and inadequate precision during surgical procedures.
Innovation Solution
Incorporation of capacitive hover sensors and AI systems with feedback loops to provide spatial coordinates and enhance navigation, along with improved localization and robot control, allowing for identification and segmentation of anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If robotic surgical arms are used for minimally invasive surgery, then surgical precision and dexterity are improved, but collision risks with anatomical structures and other instruments increase
Solution Approach 1:
The system performs preliminary actions by pre-planning surgical paths, pre-identifying anatomical structures through imaging, and pre-positioning robotic arms to avoid collision zones before surgery begins. The navigation system creates a digital twin of the patient's anatomy pre-operatively to anticipate potential collision risks.
Solution Approach 2:
The system implements real-time feedback through sensors that continuously monitor the position of robotic arms, surgical instruments, and anatomical structures. This feedback loop enables dynamic adjustment of robotic arm trajectories to avoid collisions while maintaining surgical precision, with haptic feedback providing the surgeon with tactile awareness of nearby structures.
2Reliability
If capacitive hover sensors are added to detect collisions, then safety is improved, but device complexity increases
Solution Approach 1:
The capacitive hover sensors serve multiple functions: they detect collisions between robotic arms and anatomical structures, monitor proximity to other instruments, provide haptic feedback to the surgeon, and contribute to navigation accuracy. This multi-functionality reduces the need for separate dedicated sensors for each function, thereby limiting the increase in device complexity.
Solution Approach 2:
The capacitive sensors utilize the existing electrical fields and circuitry within the robotic system components themselves, rather than requiring entirely separate sensing systems. The sensors leverage the robotic arm's own electrical infrastructure to generate and detect capacitive changes, enabling the system to sense its environment using its inherent properties.
3Measurement precision
If AI systems with feedback loops are implemented for spatial recognition, then navigation precision is improved, but computational requirements and system complexity increase
Solution Approach 1:
The AI system performs preliminary processing of medical imaging data pre-operatively to create detailed three-dimensional models and identify anatomical structures. This pre-computation reduces the computational burden during surgery, as the AI has already segmented and labeled key structures in advance, requiring only real-time tracking and registration during the procedure.
Solution Approach 2:
The system introduces an intermediary layer between the raw sensor data and the surgical control system. The AI navigation software acts as a mediator that processes complex spatial relationships, matches pre-operative imaging with intraoperative views, and translates this information into simplified guidance for the surgeon, reducing the complexity of direct system-to-system communication.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances precision and safety by preventing collisions and improving navigation and control of surgical robotic arms, enabling better identification of vascular and nervous structures during surgeries.
Implementation Method 1
the surgical robotic system disclosed herein includes capacitive hover sensors incorporated into the surgical robotic component
Data Source
AI summary
A robotic surgical system includes a surgeon consol coupled to a patient console and coupled to one or more surgical instruments. The surgeon consol is used by a surgeon to perform a surgical procedure. A surgeon computer is coupled to or at the surgeon console. The surgeon consol is coupled to the one or more surgical instruments (manipulators). A surgical robot is coupled to a robotic surgery control system and a feedback loop. The feedback loop monitors and collects data from one or more sensors used to provide feedback to the robotic surgical system. An AI system has an AI architecture that uses input data for producing an AI model. A surgical robot is coupled to a robotic surgical control system that is coupled to or includes: the feedback loop, and the artificial intelligence AI system. The control system is coupled to the surgeon consol, one or more of the feedback loop, the AI system and the control unit configured to determine a spatial configuration data of at least a portion of the one or more manipulators using image or location information of one or more of vascular or nervous structures and organs relative to the surgical intervention. One or more of the: feedback loop; AI system; and spatial configuration data used singularly or in combination to provide enhanced navigation; identification of vascular or nervous structures and organs, and manipulation of the manipulators.