Embryo Classification via Morphokinetic Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for selecting embryos for implantation in IVF procedures rely heavily on chromosome count (euploid or aneuploid classification) and lack effective prediction of implantation success, failing to consider morphokinetic and morphological factors, which are crucial for determining viability and optimal implantation time.
Innovation Solution
A system utilizing two artificial neural networks to analyze morphokinetic signatures from time-lapse images of embryos, combining morphological and morphokinetic features to predict implantation potential, viability, and determine the optimal time for transfer, thereby supplementing traditional PGS testing with empirical implantation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If chromosome count classification (euploid or aneuploid) is used for embryo selection, then the classification process is simple and quick, but the prediction accuracy of implantation success is insufficient
Solution Approach 1:
The patent combines multiple classification approaches by integrating chromosome count classification with morphokinetic analysis. The system processes both chromosomal information and time-lapse image data together to generate a comprehensive implantation success prediction, merging the simplicity of chromosomal classification with the predictive power of morphokinetic features.
Solution Approach 2:
The patent creates a composite classification model that integrates multiple data types (chromosomal classification, morphological features, and morphokinetic parameters) into a unified prediction system. This composite approach combines the strengths of different classification methods to achieve higher prediction accuracy while maintaining operational efficiency.
2Ease of operation
If static morphological features are used for embryo assessment, then the analysis is straightforward and quick, but the prediction of viability is less accurate
Solution Approach 1:
The patent transitions from static morphological assessment to dynamic morphokinetic analysis by incorporating time-lapse imaging data. The system analyzes the temporal evolution of embryo development, capturing dynamic changes in cell division, morphology, and development rate, which provides more accurate viability prediction while maintaining automated analysis simplicity.
Solution Approach 2:
The patent adds the time dimension to traditional morphological assessment by incorporating temporal information from time-lapse images. This transforms the analysis from a single static snapshot to a multi-dimensional assessment that includes developmental trajectory, rate of change, and temporal patterns, significantly improving viability prediction accuracy.
3Measurement precision
If time-lapse images and morphokinetic analysis are used, then the prediction accuracy of implantation success is improved, but the system complexity increases
Solution Approach 1:
The patent replaces complex manual morphokinetic analysis with an automated machine learning system. The neural network model automatically processes time-lapse images, extracts morphokinetic features, and generates predictions, substituting the need for complex manual evaluation protocols and expert embryologist time with an automated computational system.
Solution Approach 2:
The system performs self-service by automatically analyzing time-lapse images and generating implantation success predictions without requiring manual intervention. The machine learning model autonomously processes the data, extracts relevant features, and provides classification results, reducing the operational burden on embryologists while maintaining high prediction accuracy.
4Ease of manufacture
If manual morphological analysis is performed by embryologists, then the assessment can be performed with simple equipment, but the time consumption and subjectivity increase
Solution Approach 1:
The patent replaces manual embryologist analysis with an automated machine learning system that processes images and generates predictions. This substitution eliminates the time consumption and subjectivity associated with manual analysis while maintaining the simplicity of the imaging equipment, as the same time-lapse incubator cameras can be used but with automated analysis instead of manual evaluation.
Data Source
AI summary
Methods and systems are disclosed for improvements to determining the implantation potential of embryos. These improvements are achieved by determining a predicted implantation potential of an embryo. Calculating the predicted implantation potential can include receiving a first feature input based on a morphokinetic signature of an embryo. A first feature output can be determined based on a classification of the embryo as euploid or aneuploid. The first feature output may be input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo. The second artificial neural network may be trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data. A recommendation for implantation based on the second feature output may be generated for display at a user interface.