Programmable Light Source for Multiclass Tissue Classification
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Solution Overview
Problem
Current surgical imaging techniques face challenges in distinguishing between different tissue types, particularly in laparoscopic surgery, due to low contrast between tissues, which can lead to accidental damage of healthy tissue during cancerous tissue removal, especially near nerve tissue. Existing solutions require toxic fluorescent dyes and multiple lasers, which are costly and not FDA-approved.
Innovation Solution
A programmable light source integrated with an endoscope and a machine learning model, such as a neural network, that analyzes pixel color values to classify tissue types based on spectral illumination and reflectivity, providing real-time visualization with color overlays to differentiate tissues, reducing the need for multiple lasers and toxic dyes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If fluorescent dyes with specific excitation wavelengths are used to distinguish between different tissue types, then tissue differentiation capability is improved, but device complexity and cost increase due to requiring multiple excitation lasers and multiple images
Solution Approach 1:
A single broadband light source is used to illuminate all tissue types simultaneously, replacing the need for multiple wavelength-specific excitation lasers. The single image capture device records reflected light across the visible spectrum, eliminating the need for multiple separate imaging systems while maintaining the ability to differentiate between various tissue types through spectral analysis
Solution Approach 2:
The system changes from using specific wavelength parameters (multiple lasers at different wavelengths) to using a broadband spectrum parameter. By illuminating tissues with a continuous spectrum of wavelengths and analyzing the reflected spectral characteristics, the system achieves multiclass tissue differentiation with a single light source and single image capture device
2Difficulty of detecting and measuring
If toxic fluorescent dyes are used to achieve high contrast between tissue types, then tissue visibility is improved, but safety and biocompatibility worsen due to FDA approval requirements and potential harm to patient
Solution Approach 1:
The system converts the naturally occurring spectral reflectivity differences between tissue types, which were previously insufficient for differentiation, into useful contrast information. By using broadband illumination and spectral analysis, the system exploits the inherent optical properties of tissues without requiring exogenous contrast agents, thereby eliminating toxicity concerns while maintaining tissue visibility and differentiation capability
Solution Approach 2:
Tissues serve their own differentiation function through their natural spectral reflectivity characteristics. Each tissue type inherently reflects different portions of the broadband spectrum based on its composition and structure, allowing the system to differentiate between tissue types using the tissues' own optical properties without requiring external fluorescent dyes or contrast agents
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
This approach enhances tissue differentiation during surgery, reducing the risk of damaging healthy tissue by providing high contrast between different tissue types without the need for toxic dyes or multiple lasers, improving surgical precision and safety.
Implementation Method 1
a camera captures image data of the illuminated scene
Implementation Method 2
The machine learning model may be trained prior to use using multispectral imaging and samples of classes of tissue to determine a spectrum of illumination of the programmable light source that optimally distinguishes between the classes of tissue
Data Source
AI summary
An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.