Egg Cohort Distribution Using Blastocyst Potential Prediction
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Solution Overview
Problem
Conventional methods for cryopreserving unfertilized eggs rely on visual examination and classification based on maturity, which is inefficient and does not account for the potential to develop into blastocysts, limiting the success of in-vitro-fertilization treatments.
Innovation Solution
A machine-learning model analyzes time-lapse images of unfertilized eggs to predict their potential to become blastocysts, enabling the distribution of eggs into cohorts based on these predictions to optimize the chances of obtaining viable embryos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual examination and classification based on maturity is used for cryopreservation, then the process is simple and quick, but the accuracy of predicting blastocyst development potential is low
Solution Approach 1:
The patent replaces the manual visual examination system with an automated machine learning-based image analysis system. The machine learning model processes images of unfertilized eggs to predict blastocyst development potential, substituting human visual assessment with computational analysis that provides more accurate and consistent predictions.
Solution Approach 2:
The patent introduces time-lapse imaging as an intermediary tool that captures developmental processes of unfertilized eggs over time. These images serve as intermediate data that feed into the machine learning model, enabling indirect observation and prediction of blastocyst potential without direct manipulation of the eggs.
2Reliability
If machine learning model with time-lapse images is used, then the prediction accuracy of blastocyst development is improved, but the time and computational resources required increase
Solution Approach 1:
The patent performs image capture and preliminary processing of unfertilized eggs before fertilization occurs. Time-lapse images are collected in advance, and the machine learning model analyzes these images to predict which eggs have high potential for blastocyst development. This preliminary assessment allows for optimized cohort distribution before the actual fertilization and development process begins.
Solution Approach 2:
The patent segments the egg population into distinct cohorts based on predicted blastocyst development potential. By dividing the unfertilized eggs into high-potential and lower-potential groups, the system can prioritize resources and attention on the most promising eggs, reducing the need to process and monitor all eggs equally throughout the development process.
3Productivity
If eggs are distributed into cohorts based on predictions, then the efficiency of obtaining viable embryos is improved, but the complexity of egg management increases
Solution Approach 1:
The patent applies different management strategies to different cohorts of eggs based on their predicted potential. High-potential eggs receive priority handling and are distributed to specific recipients or fertilization batches, while lower-potential eggs are managed differently. This localized quality-based management optimizes outcomes for each subgroup without requiring complex universal protocols.
Solution Approach 2:
The patent changes the management parameters (such as fertilization timing, recipient selection, and resource allocation) based on the predicted blastocyst potential of each egg cohort. Eggs with higher predicted potential are subjected to different handling protocols compared to those with lower potential, allowing optimized outcomes through parameter adjustment rather than uniform treatment.
Data Source
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AI summary
Methods and systems are described for predicting probabilities of unfertilized eggs becoming blastocysts. For example, a method can include a trained machine-learning model receiving images of unfertilized eggs. The trained machine-learning model can determine predictions for each of the unfertilized eggs becoming blastocysts based at least on the appearance of the unfertilized eggs in the images. The predictions can be provided by the trained machine-learning model. Based on the predictions, a distribution of the unfertilized eggs into cohorts can be determined, including. Then, based on the cohorts, a second prediction of a cohort producing at least one blastocysts can be determined.