AI Surgical Overlays for Real-Time Multimodal Image Alignment
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Solution Overview
Problem
Existing robotic surgical systems lack real-time intraoperative video capture, multimodal imaging integration, and adaptive AI-driven overlays for enhanced surgical precision and safety.
Innovation Solution
A robotic surgical system with a surgical camera system for real-time video capture, an external imaging interface for multimodal data, an AI module with a trained neural network, and a display interface for dynamic overlays, allowing for continuous anatomical visualization and predictive trend visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time intraoperative video capture and multimodal imaging integration are implemented, then surgical precision and safety are enhanced, but device complexity increases
Solution Approach 1:
The system divides the imaging and processing functions into separate modules: a surgical camera system for real-time video capture, an external imaging interface for multimodal data acquisition, and an AI module for processing and overlay generation. This segmentation allows each component to be optimized independently while working together to enhance surgical precision without overwhelming complexity in a single integrated unit.
Solution Approach 2:
The AI module serves as an intermediary between the camera system, imaging interface, and the surgical field visualization. It receives data from multiple sources, processes it through neural networks, and generates overlays that are displayed to the surgeon. This intermediary layer manages the complexity of data fusion and real-time processing, presenting simplified visual information to the user.
2Adaptability or versatility
If AI module with trained neural network processes multimodal imaging data in real-time, then adaptive overlays and predictive visualization are improved, but processing time and computational resources increase
Solution Approach 1:
The neural network is pre-trained on multi-institutional annotated surgical datasets before the actual surgical procedure. This preliminary training enables the AI module to quickly process and interpret new imaging data during surgery without requiring time-consuming real-time learning. The model applies its pre-acquired knowledge to generate adaptive overlays and predictive visualizations efficiently.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the surgical context and data availability. The AI module can modify the level of processing detail, overlay complexity, and prediction granularity in real-time based on computational resources and immediate surgical needs, optimizing the balance between adaptability and processing speed.
3Reliability
If dynamic predictive trend visualization and real-time registration of imaging data are provided, then surgical safety is improved, but device complexity and data management requirements increase
Solution Approach 1:
The system continuously registers and aligns preoperative imaging data with intraoperative imaging data in real-time, creating a feedback loop that updates the visualization as the surgery progresses. This real-time registration provides continuous feedback to the surgeon about the current anatomical state relative to preoperative plans, enhancing safety through ongoing verification without requiring complex manual data management.
Solution Approach 2:
The AI module automatically performs data registration, alignment, and overlay generation without requiring manual intervention. The system self-manages the complex tasks of fusing multiple imaging modalities, tracking anatomical structures, and updating predictions based on real-time camera feeds, thereby reducing the burden on the surgical team while maintaining high reliability.
4Manufacturing precision
If overlay presentation adapts to tissue deformation without actuating robotic manipulator, then surgical precision is maintained, but system responsiveness is reduced
Solution Approach 1:
The system extracts the overlay adaptation function from the robotic manipulator control system. The AI module independently processes tissue deformation data and updates overlays without triggering robotic actuation. This separation allows the overlay presentation to adapt smoothly to anatomical changes while maintaining full responsiveness of the robotic system for when actuation is actually needed.
Data Source
AI summary
A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.