Egg and Embryo Classification with Time-Lapse Morphokinetic Signatures
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Solution Overview
Problem
Existing methods for selecting embryos for implantation, such as IVF, face challenges in accurately predicting viability due to the difficulty in distinguishing morphological states based on static images and the unpredictability of morpho-kinetic development, leading to inefficiencies in embryo selection.
Innovation Solution
A system that analyzes a series of time-lapse images of embryos to generate morpho-kinetic signatures, using deep learning models to assess and predict embryo viability by analyzing morphological and morpho-kinetic features over time, incorporating clinical and genetic data for improved prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static image analysis is used to assess embryo morphology, then the evaluation process is simple and quick, but the prediction accuracy of embryo viability is insufficient due to inability to capture morpho-kinetic development
Solution Approach 1:
The system transitions from static image analysis to dynamic time-lapse imaging, capturing morpho-kinetic events throughout embryo development. Multiple images are taken at different time points to track morphological changes, enabling more accurate viability prediction through analysis of development trajectories rather than single snapshots
Solution Approach 2:
The system adds the time dimension to traditional morphological analysis by incorporating temporal information from sequential images. Morpho-kinetic parameters are extracted by analyzing how morphological features change over time, transforming 2D spatial analysis into 3D spatio-temporal analysis that captures developmental dynamics
2Reliability
If manual analysis of morphological parameters at single time point is performed, then the process is straightforward, but it fails to capture the unpredictability of morpho-kinetic development
Solution Approach 1:
The system performs preliminary automated processing of time-lapse image series to extract morpho-kinetic parameters before final viability assessment. Deep learning models pre-process the image sequences to identify and quantify morphological changes, reducing the time required for manual analysis while improving prediction reliability through comprehensive temporal data
Solution Approach 2:
The system replaces manual embryologist analysis with automated deep learning-based image analysis. Artificial intelligence algorithms automatically detect morpho-kinetic events and extract parameters from time-lapse images, eliminating the time-consuming nature of manual frame-by-frame review while enhancing prediction reliability through consistent, objective measurement
3Measurement precision
If single visual indicator at single time period is used, then the assessment is simple, but it provides poor prediction of embryo viability compared to series of visual indicators
Solution Approach 1:
The system implements continuous imaging and monitoring of embryo development through time-lapse photography, capturing morpho-kinetic events as they occur rather than relying on discrete snapshots. This continuous data stream enables detection of subtle morphological transitions and provides a comprehensive record of developmental trajectory, improving viability prediction accuracy
Solution Approach 2:
The system introduces deep learning-based image analysis as an intermediary between raw image data and viability assessment. The automated analysis system detects and quantifies morpho-kinetic parameters from complex image series, making the detection of subtle morphological states more reliable and reducing the difficulty of interpreting time-lapse data
Data Source
AI summary
Methods and systems are described for classifying unfertilized eggs. For example, using control circuitry, first images of fertilized eggs can be received, and the first images can be labeled with known classifications. Using the control circuitry, an artificial neural network can be trained to detect the known classifications based on the first images of the fertilized eggs and a second image can be received of an unfertilized egg with an unknown classification. Using the control circuitry, the second image can be input into the trained artificial neural network and a prediction from the trained artificial neural network can be received that the second image corresponds to one or more of the known classifications.