AI Anatomical Structure Identification for Robotic Surgery
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Solution Overview
Problem
Existing robotic surgical systems lack the ability to accurately distinguish different anatomical objects from images and compare pre-operative images with current images using deep learning algorithms, which is crucial for precise surgical navigation and collision avoidance.
Innovation Solution
A robotic surgical system equipped with deep learning algorithms and image processing systems that utilize 2D pixel arrangements to classify tissue types and compare pre-operative images with current images, providing real-time feedback and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic surgical systems are equipped with deep learning algorithms and image processing systems to distinguish anatomical objects and compare pre-operative images, then surgical precision and navigation accuracy are improved, but device complexity increases
Solution Approach 1:
The system divides the image processing task into separate functional modules: pre-operative image acquisition, real-time image capture, deep learning algorithm processing, anatomical structure identification, and navigation guidance. This segmentation allows each component to be optimized independently while working together to achieve high surgical precision without overwhelming complexity in any single element
Solution Approach 2:
The deep learning algorithm acts as an intermediary between the raw images from cameras and the surgical navigation system. It processes visual data, identifies anatomical structures, and provides guidance information to the robotic system, thereby mediating between image acquisition and precise surgical execution to improve accuracy while managing system complexity
2Measurement precision
If real-time image processing and deep learning algorithms are used to identify anatomical structures during surgery, then surgical precision is improved, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by acquiring and processing pre-operative images before the actual surgical procedure. Deep learning algorithms analyze pre-operative images to identify anatomical structures and generate navigation guidance in advance, so that during surgery only real-time image capture and comparison are needed, significantly reducing real-time processing requirements while maintaining high accuracy
Solution Approach 2:
The system continuously compares real-time images with pre-operative images and uses deep learning algorithms to provide feedback on anatomical structure identification. This feedback loop allows the system to verify and adjust navigation guidance in real-time, improving accuracy while the computational burden is distributed across pre-operative planning and intraoperative verification phases
3Reliability
If capacitive hover sensors are incorporated into surgical robotic components to detect collisions, then safety and collision avoidance are improved, but device complexity and cost increase
Solution Approach 1:
The capacitive hover sensors are integrated directly into the robotic arm links and joints, allowing the robotic system to self-monitor its own position and detect collisions with the patient or surrounding objects. The sensors provide autonomous collision detection without requiring external monitoring systems, improving reliability while the sensors are built into the existing robotic structure rather than adding separate complex systems
Data Source
AI summary
A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels/Deep learning algorithms of the AI system distinguish different anatomical objects from the images.