Hyperspectral Endometrial Imaging for Non-Invasive Cycle Phase Detection
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Solution Overview
Problem
Current methods for identifying the window of implantation (WOI) in the menstrual cycle require invasive procedures like endometrial biopsy and are not suitable for immediate embryo transfer, lacking accuracy and necessitating cycle-to-cycle inference.
Innovation Solution
Utilizing hyperspectral technology and artificial intelligence to analyze endometrial spectral images across 400-1000 nm wavelengths, preprocessing, and determining distinct wavelength ranges as spectral signature biomarkers to characterize menstrual cycle phases, enabling non-invasive and accurate identification of WOI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If endometrial biopsy is used to identify the window of implantation, then measurement precision is improved, but object-affected harmful factors worsen due to invasive procedures
Solution Approach 1:
The patent replaces the mechanical invasive biopsy procedure with an optical hyperspectral imaging system. The system uses light interaction with endometrial tissue to obtain spectral signatures that reveal endometrial status, phase, and window of implantation without physical tissue removal, thereby eliminating the harmful effects of invasive procedures while maintaining diagnostic accuracy
Solution Approach 2:
The patent introduces hyperspectral imaging as an intermediary between the endometrium and the diagnostic assessment. The imaging system captures spectral information across multiple bands (400-1000 nm) that serves as a mediator to infer endometrial characteristics, phase identification, and WOI timing without direct mechanical intervention
2Measurement precision
If endometrial biopsy is used for endometrial dating, then measurement precision is improved, but loss of time worsens due to cycle-to-cycle inference requirement
Solution Approach 1:
The patent performs preliminary hyperspectral imaging assessment of the endometrium during the same menstrual cycle to identify the window of implantation before embryo transfer. This preliminary action eliminates the need to wait for the next cycle after biopsy, allowing immediate embryo transfer timing based on real-time endometrial phase characterization
Solution Approach 2:
By replacing biopsy with non-invasive hyperspectral imaging, the system enables same-cycle assessment and embryo transfer timing, eliminating the time loss associated with waiting for the next cycle that occurs with biopsy-based methods
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
Enables precise determination of the optimal embryo implantation time without cycle-to-cycle inference, facilitating immediate embryo transfer and improving pregnancy success rates through enhanced accuracy.
Implementation Method 1
hyperspectral imaging has been applied in various fields for detailed spectral analysis... capturing and processing information from a large number of spectral bands across the electromagnetic spectrum
Data Source
Figure 1A~1C
Figure 2
Figure 3A~4
AI summary
A method, system and computer programs for characterizing the menstrual cycle phases are provided. The method comprises providing/using a database of endometrial spectral images obtained from different females, the images comprising different spectral bands and belonging to an endometrial proliferative phase, to an endometrial early secretory phase, to an endometrial mid-secretory phase and to an endometrial late secretory phase; preprocessing the endometrial spectral images; analyzing differences between the endometrial phases for each wavelength position; and determining wavelength ranges distinguishing the different endometrial phases by comparing the analyzed differences between the endometrial phases, such that the determined wavelength ranges can be used as spectral signature biomarkers for the classification by artificial intelligence techniques of future images with high accuracy.