Dynamic Tissue Identification via Wavelength-Adaptive Illumination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Surgeons face difficulties in accurately identifying biological structures during surgery due to inadequate illumination and varying light sources, which can alter the perceived color of anatomical features, making it challenging to distinguish between similar tissue types.
Innovation Solution
A system utilizing a combination of specially adapted illumination with a limited set of light sources at optimal wavelengths and an image sensor to determine tissue type through Bayes' theorem, employing the Mahalanobis distance to identify clusters in N-dimensional space, and dynamically adjusting the illumination to enhance tissue discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different light sources with various wavelengths are used for illumination, then the surgical area can be viewed under different lighting conditions, but the perceived color of anatomical features changes making tissue identification difficult
Solution Approach 1:
The system changes the parameter of illumination wavelength by selecting specific wavelengths (e.g., 405nm, 480nm, 530nm, 560nm, 630nm, 690nm) to illuminate different tissue types. Each wavelength highlights specific tissue characteristics, allowing accurate identification while maintaining adaptability to various surgical conditions
Solution Approach 2:
The system dynamically switches between different wavelength configurations based on the surgical needs and tissue types being examined. The illumination wavelengths are adjusted in real-time to optimize tissue contrast and identification accuracy for different anatomical structures
2Illumination intensity
If standard broad-spectrum illumination is used, then the surgical area is well-lit, but the color perception of tissues varies and creates ambiguity in tissue differentiation
Solution Approach 1:
The broad-spectrum illumination is segmented into specific wavelength bands. Instead of using continuous broad-spectrum light, the system divides the spectrum into discrete wavelength segments (violet, blue, cyan, green, yellow-green, red) that can be selectively activated to highlight specific tissue properties without information loss
Solution Approach 2:
The system introduces an intermediary layer of spectral filtering and selection between the light source and the surgical field. This intermediary mechanism selects and transmits only the beneficial wavelength ranges that enhance tissue differentiation while blocking wavelengths that cause color ambiguity
3Measurement precision
If a limited set of specific wavelength light sources is used, then tissue discrimination is improved through reduced color ambiguity, but the system complexity increases due to multiple specialized light emitters
Solution Approach 1:
The illumination system is designed with multi-functionality where a single device can emit multiple specific wavelengths. The light source assembly can selectively activate different LED chips or laser diodes to provide various wavelength configurations, reducing overall system complexity while maintaining high tissue discrimination capability
Solution Approach 2:
The system uses adjustable parameters of the light sources (wavelength, intensity, duty cycle) to achieve multiple illumination modes from a single device configuration. By changing operational parameters rather than physically reconfiguring the system, complexity is reduced while maintaining precision
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 effectively identifies tissue types by clustering pixels in N-dimensional space, improving tissue discrimination and reducing ambiguity, even in poorly illuminated surgical environments, with the ability to dynamically adjust illumination for higher confidence in identification.
Implementation Method 1
a light source including a plurality of light emitters, each light emitter configured to emit light having a different wavelength
Implementation Method 2
a camera optically coupled to receive the light reflected back from the tissue and output image data
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An imaging system includes a light source to emit a plurality of wavelengths of light, and a camera coupled to receive the light reflected back from tissue in a body. Also included are a database with reflectance data for a plurality of tissue types, and a controller coupled to the light source, the database, and the camera. The controller causes the imaging system to perform operations, including: generating image data with the camera; receiving the image data from the camera with the controller; comparing, with the controller, the image data to the reflectance data in the database; and determining a tissue type in the image data in response to comparing the image data to the reflectance data.