Sperm DNA Fragmentation Analysis Using Brightfield AI Imaging
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Solution Overview
Problem
Existing sperm DNA fragmentation assays are destructive, generate medical waste, provide only qualitative or binary outcomes, and lack correlation with sperm viability, necessitating a non-destructive, quantitative, and viable alternative.
Innovation Solution
A method utilizing machine learning, specifically pre-trained neural networks, to analyze sperm DNA fragmentation by evaluating biomarkers in brightfield or phase contrast images, approximating the output of chemical assays without the need for chemical reagents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chemical assays are used to analyze sperm DNA fragmentation, then DNA fragmentation assessment is achieved, but the sperm cells are inactivated and cannot be used for reproductive interventions
Solution Approach 1:
The patent creates a visual copy or representation of the chemical assay results through machine learning analysis of brightfield/phase contrast images. The neural network is trained to replicate the output of chemical assays (like TUNEL, COMET, SCSA) by learning from images paired with chemical assay results, thereby providing a non-destructive alternative that copies the essential information without requiring chemical reagents that would inactivate the sperm
Solution Approach 2:
The patent replaces the chemical system (chemical reagents and staining procedures) with an optical and computational system. Instead of using chemical assays that require fluorescent stains or enzymatic reactions, the method uses brightfield or phase contrast microscopy combined with machine learning image analysis to achieve the same diagnostic information, thereby substituting chemical mechanisms with optical-computational mechanisms
2Measurement precision
If chemical assays are used for sperm DNA fragmentation analysis, then fragmentation data is obtained, but significant medical waste including chemicals is generated
Solution Approach 1:
The patent creates a visual copy or representation of the chemical assay results through machine learning analysis of brightfield/phase contrast images. The neural network is trained to replicate the output of chemical assays (like TUNEL, COMET, SCSA) by learning from images paired with chemical assay results, thereby providing a non-destructive alternative that copies the essential information without requiring chemical reagents that would inactivate the sperm
Solution Approach 2:
The patent extracts and eliminates the harmful chemical components from the diagnostic process. By removing the chemical reagents (fluorescent stains, enzymes, substrates) from the assay workflow and replacing them with optical imaging and computational analysis, the method extracts only the necessary visual information from sperm cells while discarding the waste-generating chemical steps
3Measurement precision
If chemical assays are used to assess sperm DNA fragmentation, then fragmentation status is determined, but the results are not directly correlated with sperm viability
Solution Approach 1:
The patent enhances the functionality of the imaging system to provide multiple types of information simultaneously. The machine learning model is trained to predict not only DNA fragmentation status but also sperm viability and other quality parameters from the same brightfield/phase contrast images, making the system universal and multi-functional rather than limited to a single binary classification
Solution Approach 2:
The patent transitions from the limited binary dimension of chemical assay results (fragmented vs. non-fragmented) to a multi-dimensional assessment including DNA fragmentation status, sperm viability, and quality grading. This is achieved by training the neural network to output multiple parameters and by utilizing the grayscale intensity information in brightfield/phase contrast images that contains richer biological information than binary chemical endpoints
Data Source
AI summary
The present invention relates to a method for analysing DNA fragmentation in a sperm cell by approximating the output of a pre-selected chemical assay of sperm DNA fragmentation. According to a first aspect of the present invention, there is provided a method for analysing DNA fragmentation in a sperm cell by approximating the output of a pre-selected chemical assay of sperm DNA fragmentation, the method comprising:providing an image of the sperm cell, under brightfield and/or phase contrast with a total magnification of 400× to 1000×;evaluating the image of the sperm cell to identify and/or measure a pre-selected biomarker; andapproximating the output of the pre-selected chemical assay of sperm DNA fragmentation of the sperm cell by subjecting the identified and/or measured biomarker to a first machine learning analysis.