AI Embryo Viability Prediction via CNN Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for evaluating embryo viability in IVF are subjective, costly, and lack standardization, with manual grading being intuitive and prone to variability, while existing non-manual techniques like time-lapse imaging and PGT-A face adoption barriers due to expense and invasiveness, and existing tests do not fully predict successful pregnancy outcomes.
Innovation Solution
A computer-implemented method using convolutional neural networks to analyze single images of embryos, generating viability scores that predict the likelihood of reaching clinical pregnancy, incorporating patient data and balancing training data to mitigate biases across different imaging devices and clinics, allowing for real-time evaluation and ranking of embryos without requiring specialized hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual embryo grading is performed by embryologists, then embryo selection can be conducted, but the process becomes highly subjective with varying grades depending on the embryologist
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated image analysis system using convolutional neural networks. The system processes embryo images through multiple CNN models that objectively evaluate morphological features, replacing the subjective human visual assessment with algorithm-based quantification, thereby eliminating inter-embryologist variability
Solution Approach 2:
The patent creates a digital copy of the embryo assessment process by capturing high-resolution images and processing them through trained neural networks. The CNN models learn from large datasets of labeled embryo images to replicate and standardize expert grading criteria across all assessments, ensuring consistent application of selection standards
2Measurement precision
If time-lapse imaging is used to capture embryo development sequences, then embryo assessment consistency improves, but the cost of specialized microscopes increases significantly
Solution Approach 1:
The patent extracts the essential assessment function from complex time-lapse imaging systems. Instead of requiring continuous temporal imaging, the system uses single or multiple static images captured at specific developmental stages, extracting the critical morphological information needed for assessment without the overhead of specialized time-lapse hardware
Solution Approach 2:
The patent employs standard, widely available imaging devices rather than expensive specialized microscopes. By using conventional imaging equipment that can be integrated into existing IVF workflows, the system reduces hardware costs and complexity while maintaining assessment accuracy through advanced image processing algorithms
3Measurement precision
If PGT-A testing is performed to identify euploid and aneuploid embryos, then embryo viability prediction improves, but the testing becomes invasive and may affect embryo health
Solution Approach 1:
The patent replaces invasive genetic testing with non-invasive morphological assessment using image analysis. The convolutional neural networks evaluate embryo structure, cell symmetry, and developmental characteristics to predict viability and chromosomal normality without physical intervention, eliminating biopsy-related risks while providing comparable predictive accuracy
4Adaptability or versatility
If multiple embryos are cultured to the blastocyst stage, then more selection options are available, but the cost and time of the IVF process increase
Solution Approach 1:
The patent implements preliminary assessment of embryos at earlier stages using image analysis before they reach the blastocyst stage. By evaluating morphological characteristics and predicting developmental potential during earlier culture periods, the system identifies promising embryos for continued cultivation, optimizing resource allocation and reducing unnecessary extended culture time
Data Source
AI summary
Systems and methods for predicting viability of one or more embryos is described herein. In some variations, a method may include receiving a single image of the embryo via a real-time communication link with an image capturing device and generating a viability score for the embryo by classifying the single image via at least one convolutional neural network. In some variations, a method may include receiving a plurality of single images, where each single image depicts a different respective embryo of a plurality of embryos, generating a viability score for each embryo by classifying each single image via at least one convolutional neural network, and ranking the plurality of embryos based on the viability scores for the plurality of embryos.