Embryo Viability Prediction From Time-Lapse Morpho-Kinetic Signatures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for selecting embryos for implantation, such as IVF, face challenges in accurately assessing viability due to the difficulty in identifying clear distinctions between morphological states based on static images and the unpredictability of morpho-kinetic development.
Innovation Solution
Analyze a series of time-lapse images of a developing embryo to generate a morpho-kinetic signature using deep learning models, which provides a better predictor of viability by assessing the growth rate and developmental trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single static images are used for embryo assessment, then the analysis process is simple, but the prediction accuracy of viability is insufficient
Solution Approach 1:
The patent transitions from analyzing single static 2D images to analyzing time-lapse video sequences, adding the temporal dimension. This allows the system to capture morpho-kinetic events and developmental trajectories over time, significantly improving prediction accuracy while using automated deep learning analysis to manage the increased data complexity
Solution Approach 2:
The system performs preliminary automated analysis of multiple time points during embryo development before final selection. By continuously monitoring and analyzing morpho-kinetic events at multiple stages (e.g., fertilization, cleavage, blastulation), the system builds a comprehensive developmental profile that improves viability prediction before the final implantation decision
2Measurement precision
If multiple time-point images are analyzed, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent uses automated deep learning models to create digital representations and classifications of morpho-kinetic events from video sequences. The system automatically generates standardized developmental profiles and trajectories that replicate expert embryologist assessments, reducing the manual processing difficulty while maintaining high prediction accuracy
Solution Approach 2:
The system replaces manual embryologist analysis with automated computer vision and deep learning algorithms. This substitution handles the complex processing of multiple time-point images automatically, extracting morpho-kinetic features and generating viability predictions without manual intervention, thus reducing processing difficulty while improving consistency and accuracy
3Reliability
If manual morphological analysis is performed, then the process is straightforward, but the assessment reliability is limited
Solution Approach 1:
The system continuously monitors embryo development and provides feedback through automated classification of morpho-kinetic events. By comparing observed developmental trajectories against known successful development patterns, the system continuously refines its viability assessments, improving reliability through data-driven feedback loops while maintaining automated operation
Solution Approach 2:
The deep learning model is designed to perform multiple functions: detecting morphological features, classifying developmental stages, identifying morpho-kinetic events, and predicting viability outcomes. This multi-functional automated system replaces multiple separate manual assessment processes, improving reliability through consistent application of standardized criteria across all embryos
Data Source
AI summary
Methods and systems are described for improvements in embryo selection. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used for classifying embryos.