XR Surgical Simulation with Confidence-Augmented AR Mapping
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Solution Overview
Problem
Conventional methods for preventing surgical errors and adverse events during robotic surgery are insufficient, as they rely heavily on communication between healthcare providers and are prone to errors related to wrong-site, wrong-person, or wrong-procedure mistakes, and do not effectively utilize advanced technologies for real-time monitoring and precision.
Innovation Solution
The implementation of extended reality (XR) systems, including augmented reality (AR) and machine learning (ML) algorithms, to create a digital anatomical model for surgical simulation, enable precise anatomical mapping, and assist surgical robots with real-time data analysis and workflow adjustments, thereby enhancing surgical precision and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional communication-based methods are used to prevent surgical errors, then implementation is simple and low-cost, but error prevention reliability is insufficient
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the patient's anatomy using imaging data and machine learning models. This digital anatomical model serves as a virtual replica that can be used for surgical planning, simulation, and guidance without requiring physical presence of the patient, thereby improving error prevention while managing system complexity through software-based solutions.
Solution Approach 2:
The XR system acts as an intermediary layer between the surgeon and the actual surgical field. It provides augmented reality overlays, haptic feedback, and real-time data integration that mediate the surgical interaction, enhancing safety and precision without requiring direct manual intervention for every decision.
2Measurement precision
If XR systems with digital twins are implemented, then surgical precision and error prevention improve, but system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by creating the digital anatomical model and conducting virtual surgical simulations before the actual surgery. This pre-operative preparation allows for thorough planning, identification of potential risks, and optimization of surgical approaches, thereby achieving high precision while managing complexity through advance computation rather than real-time complexity.
Solution Approach 2:
The digital twin serves multiple functions: it is used for surgical planning, virtual simulation, real-time navigation guidance, and post-operative analysis. This multi-functionality justifies the initial complexity investment by providing comprehensive precision benefits across multiple stages of the surgical workflow.
3Productivity
If virtual surgical simulation is performed, then surgical workflow optimization is achieved, but time required for pre-operative preparation increases
Solution Approach 1:
The machine learning models automatically process imaging data and generate the digital anatomical model without requiring extensive manual segmentation or annotation by radiologists or surgeons. The system performs self-service through automated image processing, algorithmic model generation, and intelligent data integration, thereby reducing the time investment required for pre-operative preparation while maintaining high workflow efficiency benefits.
Data Source
AI summary
Methods, apparatuses, and systems for performing robotic surgery in an extended-reality (XR) surgical simulation environment are disclosed. A patient anatomical model is generated. An XR surgical simulation environment is generated that includes the patient anatomical model. The XR surgical simulation environment is configured to enable the user to virtually perform surgical steps on the patient anatomical model. Anatomical mapping input is received from a user viewing the patient anatomical model. Confidence-score augmented-reality (AR) mapping is performed to meet a confidence threshold for a procedure to be performed on the patient. A portion of the received anatomical mapping data is selected for AR mapping to an anatomy of the patient. The selected anatomical mapping data is mapped to corresponding anatomical features. An AR environment is displayed to the user, wherein the AR environment includes the mapping of the selected anatomical mapping data to the corresponding anatomical features.