Embryo Morpho-Kinetic Signatures for Viability Classification
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Solution Overview
Problem
Existing embryo selection methods for implantation in IVF treatments face challenges due to the difficulty in identifying clear distinctions between morphological states based on static images and the unpredictability of morpho-kinetic development, leading to suboptimal embryo selection.
Innovation Solution
Analyze a series of time-lapse images to generate morpho-kinetic signatures using deep learning models, which provide a comprehensive assessment of embryo development by recognizing visual indicators and predicting viability through a series of morpho-kinetic events, incorporating additional data such as PGD, PGS, and clinical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static images are used for embryo assessment, then the analysis is simple and quick, 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 viability prediction accuracy while using deep learning models to manage the increased data complexity
Solution Approach 2:
The system shifts from static image analysis to dynamic video analysis, capturing the temporal evolution of embryo morphology. By analyzing sequences of images and tracking morpho-kinetic events over time, the system extracts dynamic features that improve prediction accuracy while using neural networks to process the temporal data efficiently
2Reliability
If multiple morpho-kinetic events are tracked over time, then the viability prediction improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the embryo development process into distinct morpho-kinetic events (e.g., cell divisions, compaction, blastulation). Each event is detected and timestamped independently, allowing the system to track multiple developmental milestones over time. This segmentation approach improves prediction reliability by capturing key developmental transitions while making the complex temporal data more manageable for analysis
3Measurement precision
If deep learning models are used to analyze time-lapse images, then the classification accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary processing of time-lapse images by detecting and annotating morpho-kinetic events before final classification. Deep learning models are trained on pre-processed data with labeled events, which reduces the computational burden during actual embryo assessment. This preliminary action maintains high classification accuracy while reducing processing time for clinical decision-making
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 to classifying embryos.