Bladder Tissue Imaging for Thermal Denaturation Depth Detection
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Solution Overview
Problem
Existing medical procedures for transurethral resection of bladder tumors struggle to accurately distinguish between different layers of the bladder wall, such as the mucosal, muscle, and fat layers, during surgical resection, leading to potential inaccuracies in tumor removal.
Innovation Solution
A medical device and system that utilizes multiple light sources to generate images distinguishing between tissue layers and thermal denaturation information, employing machine learning to determine the depth and presence of thermal denaturation in bladder tissue layers, enhancing surgical precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-light imaging is used, then the device complexity is low, but the layer discrimination accuracy is insufficient
Solution Approach 1:
The patent divides the imaging function into separate modules: a first imaging unit for capturing reflected light images and a second imaging unit for capturing fluorescence images. Each unit uses specific wavelength ranges optimized for detecting particular tissue layers, enabling accurate layer discrimination without requiring a single complex imaging system
Solution Approach 2:
The imaging device is designed to perform multiple functions by capturing both reflected light images and fluorescence images using the same endoscope structure. This multi-functionality allows the system to detect different tissue characteristics (mucosal layer, muscle layer, fat layer) simultaneously, improving layer discrimination accuracy while maintaining reasonable device complexity
2Measurement precision
If thermal denaturation detection is added, then the measurement precision for thermal damage assessment is improved, but the device complexity increases
Solution Approach 1:
The patent uses fluorescence as an intermediary indicator to detect thermal denaturation. Instead of directly measuring thermal damage, the system detects fluorescence emitted from advanced glycation end products (AGEs) that form during thermal denaturation. This intermediary approach enables precise thermal damage assessment using existing fluorescence imaging capabilities, avoiding the need for complex direct thermal measurement systems
Solution Approach 2:
The system detects thermal denaturation by monitoring changes in fluorescence emission intensity and wavelength characteristics. By analyzing parameter changes in the fluorescence signal (intensity, spectral shape) rather than directly measuring temperature or tissue damage, the system achieves high measurement precision with relatively simple detection hardware
3Measurement precision
If machine learning processing is implemented, then the measurement precision for depth determination is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary image acquisition and feature extraction before machine learning processing. The imaging units capture images with specific wavelength ranges optimized for tissue layer detection, and the system pre-processes these images to extract relevant features (fluorescence intensity, spectral characteristics). This preliminary preparation reduces the computational burden during machine learning inference, thereby decreasing overall processing time while maintaining high depth determination accuracy
Solution Approach 2:
The system creates a digital copy of the tissue structure through fluorescence imaging and processes this copy using machine learning algorithms. By working with the fluorescent image data rather than directly manipulating complex 3D tissue models or requiring real-time physical measurements, the system achieves accurate depth determination with reduced processing time
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
Improves surgical accuracy by providing detailed layer discrimination and thermal denaturation detection, enabling more precise tumor resection and reducing the risk of tissue damage.
Implementation Method 1
a first light source configured to generate first light for acquiring layer information of a biological tissue
Implementation Method 2
a second light source configured to generate excitation light that excites advanced glycation end products generated by performing heat treatment on the biological tissue
Data Source
AI summary
A medical device includes a processor configured to acquire a first image that includes layer information of a biological tissue including a plurality of layers, acquire a second image that is a fluorescence image and includes thermal denaturation information regarding thermal denaturation caused by heat treatment on the biological tissue, acquire correlation information indicating a preset relationship between an emission intensity and a depth of thermal denaturation from a surface layer in the biological tissue, determine a presence or absence of the thermal denaturation of a predetermined layer in the biological tissue based on the first image and the second image, determine a depth of thermal denaturation of the predetermined layer from the surface layer in the biological tissue based on an emission intensity of the fluorescence image and the correlation information, and output depth information regarding the determined depth.