AI Fluorescent Image Color Coding for Surgical Structure Differentiation
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Solution Overview
Problem
Surgeons face challenges in interpreting and correctly using multiple fluorescent dyes during surgical procedures due to the need for familiarity with specific dye characteristics and coloring schemes, which increases the learning curve and risk of misinterpretation, especially when multiple dyes are used simultaneously.
Innovation Solution
A method and system utilizing artificial intelligence models trained on fluorescent images to identify structures by their dye emission and perform color coding, allowing for real-time processing and overlay of fluorescent images onto non-fluorescent images, using pre-determined false colors to differentiate structures, thereby simplifying the visualization and reducing the learning curve for surgeons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple fluorescent dyes are used to highlight different structures, then the ability to differentiate structures is improved, but the complexity of interpretation and learning curve increase
Solution Approach 1:
The patent applies different false colors to represent different fluorescent dyes and their corresponding structures. Each dye type is assigned a specific color code that appears in the composite image, allowing surgeons to quickly differentiate between multiple structures without needing to memorize complex dye characteristics. This visual color-coding system transforms multiple fluorescent signals into an intuitive color-based legend.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically identifies fluorescent structures, determines which dye type is present, and assigns appropriate colors based on a predetermined scheme. This intermediary AI model acts as a mediator between the raw fluorescent signals and the surgeon's interpretation, eliminating the need for surgeons to directly learn dye properties.
2Ease of operation
If fluorescent images are overlaid with false colors to improve visualization, then the ease of operation is improved, but the loss of original information may increase
Solution Approach 1:
The composite image system serves multiple functions simultaneously: it preserves the original fluorescent signal information for scientific accuracy while applying false colors for enhanced visual differentiation. The system maintains the integrity of the fluorescent data while adding the interpretive layer of color-coding, allowing both raw information and processed visualization to coexist in a single image.
Solution Approach 2:
The system changes the visual parameter of color representation by applying false colors to fluorescent structures. This parameter transformation enhances visibility and differentiation while the underlying fluorescent intensity and spectral information are preserved in the image data, allowing quantitative analysis to remain accurate despite the visual transformation.
3Productivity
If AI models are used to automatically identify and color-code structures, then the productivity is improved, but the device complexity increases
Solution Approach 1:
The AI model performs self-service by automatically analyzing fluorescent patterns, identifying structures, determining dye types, and assigning colors without requiring manual intervention or configuration by the surgeon. The system serves itself by having the AI model learn from training data and autonomously make interpretation decisions, reducing the need for complex user interfaces or manual setup procedures.
Solution Approach 2:
The AI model is pre-trained on extensive fluorescent image data before deployment, performing preliminary learning and pattern recognition in advance. This preliminary training allows the model to quickly and accurately identify structures and assign appropriate colors during actual surgical procedures without requiring real-time complex processing or manual configuration, thus improving intraoperative productivity.
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
The system enables immediate recognition and differentiation of structures, reducing the risk of misinterpretation and the time spent learning new dyes by presenting intuitive and consistent visual information, enhancing surgical safety and efficiency.
Implementation Method 1
Fluorescence imaging is a form of molecular imaging, which generally encompasses imaging methods for visualizing and/or tracking of molecules having specific properties that are used for molecular imaging. Such molecules can be substances that are endogenous to the body, or dyes or contrast agents that are injected into the patient. MRI and CT, for example, therefore, also fall under the term 'molecular imaging'. Fluorescence imaging as a variant of molecular imaging uses the property of certain molecules (fluorophores), which emit light of certain wavelengths when excited by light of certain excitation wavelengths.
Implementation Method 2
For the purpose of fluorescence imaging, the system's imaging system, e.g., a camera head, typically includes sensors that are sensitive in the visible spectrum and in the near infrared spectrum, but may also cover other spectra, depending on the dye used.
Data Source
AI summary
A method for processing fluorescent image guided surgical or diagnostics imagery. The method including: capturing non-fluorescent images and fluorescent images of an operating field in which at least one fluorescent dye is present with a surgical or diagnostics imaging device, processing the fluorescent images with respect to brightness and coloring, generating composite images by overlaying the fluorescent images over the non-fluorescent images. Wherein the processing of the fluorescent images includes inputting the fluorescent images into at least one artificial intelligence model trained on one or more of fluorescent still images and fluorescent video images to identify one or more types of structures by fluorescent dye emission. The method further includes performing color coding of the fluorescent images by coloring fluorescent parts of the fluorescent images with pre-determined false colors assigned to different types of structures according to the respective one or more identified types of structures.