Machine Learning Sperm Imaging for Non-Destructive DNA Fragmentation Analysis
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Solution Overview
Problem
Existing sperm DNA fragmentation assays are destructive, generate medical waste, provide only qualitative or binary outcomes, and cannot be directly correlated with sperm viability, necessitating a non-destructive, quantitative, and commonly available method for assessing sperm quality.
Innovation Solution
A method utilizing machine learning, specifically pre-trained neural networks, to analyze sperm DNA fragmentation by evaluating images of sperm cells under brightfield or phase contrast microscopy, identifying biomarkers, and 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 assess sperm DNA fragmentation, then DNA fragmentation can be detected, 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 output through machine learning analysis of brightfield/phase contrast images. The neural network is trained to replicate the DNA fragmentation assessment that would be obtained from chemical assays, allowing the same information to be extracted without using destructive chemicals. This enables both accurate DNA fragmentation detection and preservation of sperm viability.
2Measurement precision
If chemical assays are used for sperm DNA fragmentation analysis, then fragmentation data can be obtained, but significant medical waste including chemicals is generated
Solution Approach 1:
The patent replaces the chemical-based assay system with a computational image analysis system. Instead of using chemical reagents that require disposal, the method uses machine learning algorithms to analyze standard microscope images. This substitution eliminates the generation of chemical waste while maintaining the ability to assess DNA fragmentation, as the neural network processes visual data to predict fragmentation levels.
3Measurement precision
If conventional chemical assays are used, then DNA fragmentation can be measured, but the results are limited to qualitative or binary outcomes rather than quantitative data
Solution Approach 1:
The patent transforms the output from qualitative/binary categories to continuous quantitative values by using the neural network to predict DNA fragmentation as a numerical parameter. The model outputs a fragmentation score that reflects the degree of fragmentation on a continuous scale, providing richer information than simple fragmented/non-fragmented classification. This enables more nuanced assessment and better correlation with sperm viability.
4Measurement precision
If chemical assays are performed to assess sperm quality, then DNA fragmentation data can be obtained, but the sperm cells are rendered unusable for IVF or ICSI procedures
Solution Approach 1:
The patent creates a visual copy or representation of the chemical assay output through machine learning analysis of brightfield/phase contrast images. The neural network is trained to replicate the DNA fragmentation assessment that would be obtained from chemical assays, allowing the same information to be extracted without using destructive chemicals. This enables both accurate DNA fragmentation detection and preservation of sperm viability.
5Reliability
If machine learning analysis is used to approximate chemical assay output, then non-destructive quantitative assessment is achieved, but the method requires training data from chemical assays
Solution Approach 1:
The patent performs preliminary training of the neural network model using paired data from chemical assays and corresponding images. This preliminary action creates a pre-trained model that can then be deployed for non-destructive analysis. The training phase, while requiring chemical assay data, is a one-time investment that enables subsequent non-destructive testing without ongoing chemical consumption.
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.