Automated Embryo Evaluation Using Deep Learning Neural Networks
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Solution Overview
Problem
Current embryo selection methods for assisted reproductive technologies (ART) are inefficient, subjective, and dependent on human expertise, leading to low success rates and high costs, as they rely on visual assessments that can harm embryos and lack predictive accuracy.
Innovation Solution
A system using a convolutional neural network and an expert system for automated embryo evaluation, which acquires images of embryos and generates values for current and future quality, implantation likelihood, and live birth potential, without human intervention, employing pre-trained deep neural networks for embryo classification and analysis at clinically relevant stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual embryo morphological assessment is used, then embryo selection can be performed, but the method is highly practice dependent and subjective, leading to low success rates
Solution Approach 1:
The patent replaces manual visual assessment by embryologists with an automated deep learning system that uses convolutional neural networks to analyze embryo images. This substitution eliminates subjectivity and practice-dependency while maintaining or improving assessment accuracy through consistent application of learned patterns across all evaluations.
Solution Approach 2:
The system creates a digital model of expert embryologist assessment capabilities by training deep neural networks on large datasets of labeled embryo images. The trained network captures and replicates expert decision-making patterns, enabling automated evaluation that matches or exceeds human performance without requiring human expertise for each assessment.
2Measurement precision
If manual embryo assessment is performed, then embryo quality can be evaluated, but embryos are subject to removal from tightly controlled culture environments, which affects embryos negatively
Solution Approach 1:
The patent replaces physical manual handling and visual inspection with non-invasive automated image analysis. Deep learning models process digital embryo images to perform quality assessment without requiring removal of embryos from controlled culture environments, thereby eliminating environmental disturbance while maintaining evaluation capability.
Solution Approach 2:
The system introduces an intermediary layer of automated image analysis between the embryo and the assessment process. Instead of direct human interaction requiring embryo manipulation, digital images serve as intermediaries that capture embryo characteristics for analysis by deep learning algorithms, preventing harmful environmental exposure.
3Reliability
If traditional IVF methods are used, then pregnancy can be achieved, but the success rate is low and multiple cycles are required, increasing cost and time
Solution Approach 1:
The patent applies preliminary action by performing accurate embryo selection and predicting implantation outcomes before embryo transfer. The deep learning system evaluates embryos at early stages and identifies those with highest implantation potential, allowing clinicians to make informed decisions about which embryos to transfer, thereby improving success rates and reducing the need for multiple cycles.
Solution Approach 2:
The system incorporates feedback mechanisms by using historical outcome data to continuously improve embryo selection accuracy. By analyzing results from previous transfers and implantation outcomes, the deep learning models refine their predictions and selection criteria, progressively improving ART success rates through data-driven optimization.
Data Source
AI summary
Systems and methods are provided for provided for automatic evaluation of a human embryo. An image of the embryo is obtained and provided to a neural network to generate a plurality of values representing the morphology of the embryo. The plurality of values representing the morphology of the embryo are evaluated at an expert system to provide an output class representing one of a current quality of the embryo, a future quality of the embryo, a likelihood that implantation of the embryo will be successful, and a likelihood that implantation of the embryo will result in a live birth.