AI Oocyte Image Analysis for Reproductive Potential Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack a validated, non-invasive oocyte classification system to accurately predict reproductive potential, relying on historical data rather than individual oocyte assessment, which limits the ability to determine fertilization, blastocyst development, chromosomal normality, and implantation potential.
Innovation Solution
A method utilizing artificial intelligence and cognitive computing for non-invasive image analysis of oocytes through a light microscope, correlating image features with reproductive outcomes to predict fertilization, blastocyst development, chromosomal normality, and implantation potential from a single image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If historical data based on age and number of mature oocytes is used for prediction, then prediction can be performed without individual assessment, but prediction accuracy and personalized insight are insufficient
Solution Approach 1:
The patent creates a digital copy of the oocyte through high-resolution imaging, capturing morphological features that can be analyzed by AI algorithms. This digital representation allows for detailed individual assessment without requiring physical manipulation or invasive procedures on the actual oocyte.
Solution Approach 2:
The patent replaces manual embryologist assessment with an AI-based automated analysis system. The AI algorithm processes images and predicts reproductive outcomes, substituting human visual inspection and decision-making with machine learning-based prediction models that provide more consistent and objective evaluations.
2Device complexity
If no validated oocyte classification system is used, then current methods can be simpler, but accurate prediction of reproductive potential is not achieved
Solution Approach 1:
The patent transforms the assessment approach by changing from subjective morphological grading to quantitative parameter extraction through AI analysis. The system extracts multiple morphological parameters from images and uses these as inputs for prediction models, enabling more reliable and standardized evaluation of oocyte quality.
Solution Approach 2:
The patent introduces an AI-based image analysis system as an intermediary between the oocyte sample and the prediction outcome. This intermediary processes the visual information and translates it into predictive metrics, providing a validated classification framework that bridges the gap between simple imaging and complex reproductive potential assessment.
3Measurement precision
If invasive methods are used for oocyte assessment, then detailed internal quality metrics can be obtained, but the oocyte may be damaged or altered
Solution Approach 1:
The patent replaces invasive mechanical or chemical assessment methods with non-invasive optical imaging and AI analysis. By using light microscopy and image processing, the system obtains detailed morphological information without physically contacting or chemically treating the oocyte, thereby avoiding potential damage.
Solution Approach 2:
The patent creates a digital copy of the oocyte through high-resolution imaging, allowing comprehensive analysis of morphological features without touching or altering the actual biological sample. This digital replica enables repeated analysis and various measurement approaches without risking oocyte integrity.
Data Source
AI summary
Methods and systems for determining quality of an oocyte to reach various reproductive milestones, including fertilizing, developing into a viable embryo (blastocyst), implanting into the uterus, and reaching a clinical pregnancy, through visual assessment (non-invasive) from a single image using artificial intelligence software.