Surgical Guidance System Using Multi-Modal Imaging for Precision
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Solution Overview
Problem
Conventional surgical guidance systems based on the human visual spectrum are insufficiently precise and accurate due to variations in patient anatomy, pathology, surgical techniques, and surgeon proficiency, making them impractical for clinical applications.
Innovation Solution
The development of artificial intelligence (AI)-based systems that utilize machine learning algorithms to provide real-time augmented visuals and decision support during surgical procedures, incorporating multiple imaging modalities such as white light, near-infrared, and laser speckle-based images to predict and identify critical structures and tissue viability, and autonomously track deformable tissue targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional surgical guidance systems use standard visual spectrum imaging, then the system is simple and easy to operate, but the precision and accuracy of anatomic location prediction is insufficient
Solution Approach 1:
The patent combines multiple imaging modalities (white light, near-infrared, laser speckle) into a single surgical guidance system. This merging of different imaging techniques allows the system to overcome the limitations of individual modalities and achieve superior precision in anatomic location prediction while managing complexity through integrated processing.
Solution Approach 2:
The patent extends imaging beyond the traditional visible spectrum into the near-infrared dimension. By incorporating NIR and laser speckle imaging, the system accesses additional spectral dimensions that provide complementary information about tissue properties and anatomic structures, thereby improving measurement precision.
2Measurement precision
If conventional systems rely on human visual spectrum imaging, then the device complexity is low, but the accuracy of critical structure identification is insufficient due to patient anatomy variations
Solution Approach 1:
The patent creates a multi-functional imaging system that can operate across multiple spectral ranges (visible, near-infrared) and modes (reflectance, fluorescence, laser speckle). This universal system can adapt to different patient anatomies and surgical scenarios, improving the accuracy of critical structure identification while managing complexity through unified processing architecture.
Solution Approach 2:
The patent changes the imaging parameters by operating in different spectral ranges and imaging modes. By adjusting wavelength, illumination type, and detection parameters, the system can optimize performance for different tissue types and surgical conditions, thereby improving identification accuracy across varying patient anatomies.
3Reliability
If multiple imaging modalities are integrated, then the measurement precision and tissue viability assessment improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex imaging data from multiple modalities into distinct processing streams for white light, near-infrared, and laser speckle imaging. Each stream is processed independently using appropriate algorithms, then the results are integrated to provide comprehensive tissue viability assessment. This segmentation manages data processing complexity while maintaining high reliability.
Solution Approach 2:
The patent introduces AI-based image analysis as an intermediary layer between the multiple imaging modalities and the final surgical guidance output. This intermediary processes and integrates data from all modalities, extracting meaningful information about tissue viability while shielding the surgeon from the underlying data processing complexity.
4Productivity
If real-time multi-modal imaging is implemented, then surgical decision support quality improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of imaging data through AI-based analysis pipelines that are prepared and trained beforehand. During surgery, the pre-configured algorithms rapidly process incoming multi-modal images, providing real-time surgical decision support without requiring complex computations during the critical surgical moments, thus minimizing processing time loss.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances surgical precision and safety by providing real-time predictive visualizations and decision support, improving the accuracy of critical structure identification and tissue viability assessment, and facilitating more effective surgical planning and execution.
Implementation Method 1
chemical signal enhancers such as fluorescent near-infrared (NIR) images and/or videos
Implementation Method 2
optically enhanced signals such as laser speckle-based images and/or videos
Data Source
AI summary
The present disclosure provides systems and methods for providing surgical guidance. In one aspect, the present disclosure provides a surgical guidance system comprising: an image processing module configured to (i) receive image data obtained using one or more image and/or video acquisition modules and (ii) generate one or more medical predictions or assessments based on (a) the image/video data or physiological data associated with the image/video data and (b) one or more training data sets comprising surgical or medical data associated with a patient or a surgical procedure. The one or more training data sets may comprise anatomical and physiological data obtained using a plurality of imaging modalities. The system may further comprise a visualization module configured to provide a surgical operator with an enhanced view of a surgical scene, based on the one or more augmented data sets.