Machine Learning Sperm Selection for Objective Candidate Identification
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Solution Overview
Problem
Current sperm selection methods for infertility treatment are inaccurate and subjective, relying on rough morphological aspects and motility, which can affect the quality and success of fertilization procedures.
Innovation Solution
A machine learning model trained on image data of semen samples, using qualitative parameters to identify spermatozoa candidates for fertilization procedures, incorporating features like motility, morphology, and vacuole presence, and providing automated detection and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sperm selection is performed by technicians, then flexibility and adaptability are maintained, but measurement precision and reliability deteriorate due to subjectivity and human error
Solution Approach 1:
The patent replaces the manual mechanical selection process performed by technicians with an automated machine learning-based image analysis system. The system captures images of spermatozoa, processes them through trained models to identify morphological features and motility patterns, and automatically selects candidate spermatozoa for fertilization procedures, thereby eliminating human subjectivity and improving measurement precision.
2Reliability
If automated machine learning-based selection is implemented, then measurement precision and reliability improve, but device complexity increases due to the need for training data and model infrastructure
Solution Approach 1:
The patent implements a preliminary training phase where machine learning models are trained on extensive datasets of labeled spermatozoa images with known qualitative assessments. This pre-training process establishes the foundation for consistent and reliable automated selection, allowing the system to achieve high reliability by having already learned from diverse examples before actual selection operations begin.
3Manufacturing precision
If comprehensive qualitative assessment parameters are used, then manufacturing precision improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the complex qualitative assessment into multiple distinct morphological parameters including head shape, midpiece characteristics, tail structure, and motility patterns. Each parameter is detected and measured separately by specialized image processing algorithms, making the overall complex assessment manageable and improving selection quality through comprehensive evaluation of individual features.
Data Source
AI summary
A method comprising: receiving image data associated with a plurality of semen samples; at a training stage, training a machine learning model on a training set comprising: (i) said image data, and (ii) labels associated with a qualitative assessment of each of one or more individual spermatozoa in said semen samples; and applying said trained machine learning model to target image data associated with a target semen sample, to identify one or more spermatozoa in said target sample as candidates for an ART procedure.