Robotic Surgical Navigation via AI Angiography
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Solution Overview
Problem
Current surgical procedures face high adverse event rates due to communication breakdowns, diagnostic delays, and technical errors, which conventional methods are insufficient to prevent, especially in the absence of skilled radiologists during robotic surgeries.
Innovation Solution
The implementation of a robotic surgical system using multiple-wavelength imaging and machine learning for real-time digital image analysis to navigate surgical instruments through a patient's vasculature, enabling precise diagnosis and treatment of conditions like blood clots and hemorrhages without the need for a skilled radiologist, by processing images from various modalities and guiding the surgical robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional communication-based methods are used for surgical safety, then simplicity and ease of operation are maintained, but reliability and ability to prevent surgical errors deteriorate
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the surgical team and patient safety. This system processes medical images to detect anatomical structures, tumors, and surgical landmarks, providing objective guidance that supplements human communication and decision-making, thereby improving reliability without requiring complete system redesign
Solution Approach 2:
The image analysis system performs self-service by automatically analyzing medical images and providing diagnostic information without requiring constant human intervention. The system independently identifies anatomical features, detects pathologies, and guides surgical navigation, reducing reliance on continuous human communication while maintaining high safety standards
2Measurement precision
If skilled radiologists are present during robotic surgeries for real-time interpretation, then diagnostic accuracy and measurement precision improve, but productivity and surgical time deteriorate
Solution Approach 1:
The patent replaces the mechanical system of human radiologist interpretation with an automated image analysis system using machine learning and computer vision algorithms. This substitution maintains diagnostic accuracy by providing real-time automated analysis while eliminating the time delays associated with human consultation, thereby improving both precision and productivity
Solution Approach 2:
The system performs preliminary action by pre-processing and pre-analyzing medical images before the surgical procedure begins and during the procedure in real-time. This allows diagnostic information to be prepared in advance and made immediately available to the surgical team, eliminating wait times while maintaining high diagnostic accuracy
3Measurement precision
If multiple-wavelength imaging and machine learning analysis are implemented, then measurement precision and diagnostic accuracy improve, but device complexity and energy consumption increase
Solution Approach 1:
The patent applies segmentation by dividing the complex imaging and analysis system into distinct functional modules: multiple-wavelength imaging subsystems, image preprocessing modules, machine learning analysis components, and output generation systems. This modular segmentation manages complexity by allowing each component to be optimized independently while maintaining overall system accuracy
4Productivity
If automated image analysis is used to navigate surgical instruments, then productivity and surgical time improve, but measurement precision and navigation accuracy may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the automated image analysis system continuously monitors surgical instrument position, compares it with the planned trajectory, and provides real-time corrections. This closed-loop feedback ensures that productivity gains from automation do not compromise navigation accuracy, as the system self-corrects any deviations
Data Source
AI summary
Methods, apparatuses, and systems for digital image analysis for device navigation in tissue are disclosed. The disclosed system uses real-time angiography and artificial intelligence to navigate an end effector of a surgical robot through a patient's vasculature to provide a surgical intervention. Digital imaging is performed that enables three-dimensional mapping of the patient's vasculature. Locations and movement of the end effector of the surgical robot are determined. The end effector is used to perform an intervention such as the removal of a blood clot or delivery of a drug for dissolving a blood clot.