Robotic Surgical Arm Control Using AI Anatomy Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic surgery systems lack enhanced imaging, improved treatment planning, risk assessment, robot-assisted navigation, autonomous robotics, intraoperative decision support, and continuous learning capabilities, which are essential for precise and adaptive surgical procedures.
Innovation Solution
A robotic surgery system equipped with a surgeon console, image recognition database, and machine learning algorithms that analyze intraoperative data in real-time to identify anatomical structures, adjust robotic arm movements, and provide intraoperative decision support, enabling precise targeting and minimizing tissue damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms are integrated into the robotic surgical system to enable real-time image recognition and autonomous decision-making, then surgical precision and safety are improved, but device complexity increases
Solution Approach 1:
The system divides the complex surgical task into multiple independent processing modules: image acquisition module, machine learning analysis module, treatment planning module, and robotic control module. Each module handles a specific function, allowing the system to achieve high surgical precision through specialized processing while managing overall complexity through modular architecture.
Solution Approach 2:
A computing device acts as an intermediary between the robotic surgical system and the surgeon. This intermediary processes intraoperative images through machine learning algorithms, generates treatment plans, and provides real-time guidance, thereby enhancing surgical precision without requiring the surgeon to directly manage the complexity of the AI systems.
2Manufacturing precision
If machine learning algorithms analyze intraoperative data in real-time to identify anatomical structures and adjust robotic arm movements, then surgical precision is improved, but computing resources and time are consumed
Solution Approach 1:
The system performs preliminary processing of intraoperative images by the machine learning algorithm before the surgeon needs to make decisions. The computing device pre-identifies anatomical structures, pre-generates treatment plans, and pre-adjusts robotic arm parameters, so that when surgical action is required, the precision-enhancing computations are already complete, minimizing real-time delays.
3Ease of operation
If the robotic surgical system incorporates autonomous robotics with AI control, then dexterity and precision are improved, but the extent of automation increases system complexity
Solution Approach 1:
The robotic surgical system incorporates self-service capabilities through autonomous AI control. The machine learning algorithms automatically analyze images, identify anatomical structures, adjust robotic arm movements, and optimize surgical parameters without continuous human intervention. This autonomy enhances dexterity and precision while managing complexity through self-regulating systems that adapt to surgical conditions in real-time.
Data Source
AI summary
A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures. In response to detection of the anatomical structures the AI system at least partially adjusts movement of the robotic surgical arms to avoid critical anatomical structures while performing the robotic surgery procedure to ensure precise targeting at the surgical site while minimizing damage to surrounding tissue at a surgical site. The AI system provides a surgeon with improved dexterity when the surgeon uses the robotic surgical arms at the surgical site, the improved dexterity resulting from at least partially analyzing the intraoperative data in real time by the one or more machine learning algorithms, enabling precise and adaptive manipulation of the robotic surgical arms at the surgical site.