Automated Gamete Selection via Video Tracking and ML
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Solution Overview
Problem
Conventional gamete selection methods for IVF are labor-intensive, have low throughput, and lack the ability to select high-quality gametes effectively, especially in low-fertility samples, due to reliance on manual analysis and lack of models that incorporate population-level information for individual gamete selection.
Innovation Solution
An automated gamete selection method that involves sampling videos of gametes, tracking their movement, determining attribute values, and selecting gametes based on aggregated attributes using trained models, enabling real-time, concurrent analysis of multiple gametes and improving the selection process through machine learning and specialist expertise integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gamete selection by medical professionals is used, then expertise and domain experience are applied, but throughput is very low and only a small proportion of candidates can be analyzed
Solution Approach 1:
The patent replaces manual mechanical analysis by medical professionals with an automated computer-based system that captures images of gametes, extracts features, and uses machine learning models to select high-quality gametes. This substitution enables analysis of millions of gametes rather than the limited manual capacity, resolving the throughput bottleneck while maintaining selection quality through expert-trained algorithms.
2Measurement precision
If manual analysis of gametes is performed, then individual evaluation can be conducted, but the probability of selecting high-quality gametes is lowered due to limited candidate consideration
Solution Approach 1:
The patent creates a computational model that copies and learns from the decision-making patterns of expert medical professionals through training on labeled datasets. This model can then evaluate and select gametes consistently across large populations, ensuring that the same high-quality selection criteria are applied to all candidates without human fatigue or variability, thereby increasing selection reliability.
3Ease of operation
If conventional gamete selection methods are used, then simple manual processes are employed, but the ability to select between gametes defective in different ways is lacking
Solution Approach 1:
The patent extracts and evaluates multiple local features of individual gametes including head morphology, midpiece characteristics, and tail structure. The machine learning model assesses each feature independently and integrates them to identify high-quality gametes even when some features show minor defects. This localized feature analysis enables nuanced selection capabilities that conventional manual methods cannot achieve.
4Productivity
If automated systems are implemented to increase throughput, then more gametes can be analyzed, but complexity of the system increases
Solution Approach 1:
The patent divides the automated gamete selection system into distinct modular components: image capture module, feature extraction module, machine learning model module, and selection output module. Each module performs a specific function and can be independently optimized or replaced. This segmentation manages system complexity while enabling high throughput processing through parallel operation of the modular components.
Data Source
AI summary
In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.