Embryo Morphology AI Evaluation for IVF Quality Assurance
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Solution Overview
Problem
Current computer vision methods for embryo and oocyte assessment in assisted reproduction are semi-automated and require intensive image preprocessing, leading to labor-intensive and subjective quality assessments that are inefficient and time-consuming, lacking objective systems for evaluating oocyte quality and predicting developmental potential.
Innovation Solution
Employing deep neural networks, such as convolutional neural networks, for automated feature selection and analysis of reproductive cellular structures, enabling objective quality assurance metrics without human intervention, including image acquisition, morphological evaluation, and identity verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are employed for automated feature selection and analysis, then objectivity and accuracy of quality assessment is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent replaces manual, subjective quality assessment mechanisms with automated deep neural network systems. The neural networks process images of reproductive cellular structures and generate quality assurance metrics objectively, eliminating human subjectivity and labor-intensive preprocessing while maintaining or improving assessment accuracy.
2Productivity
If automated neural network systems are used for quality assessment, then labor intensity and time consumption are reduced, but reliability and consistency of results depend on system calibration
Solution Approach 1:
The patent implements feedback mechanisms where quality assurance metrics generated by the neural networks are compared against predefined standards and thresholds. This feedback loop ensures that automated assessments maintain reliability and consistency by validating results against established criteria, allowing for system calibration and quality control.
3Measurement precision
If intensive image preprocessing is performed in semi-automated systems, then measurement detail is improved, but time consumption and computational load increase
Solution Approach 1:
The patent applies preliminary action by pre-training deep neural networks on extensive datasets of reproductive cellular structures. This pre-training enables the networks to perform accurate morphological evaluation directly on input images without requiring intensive preprocessing steps, significantly reducing processing time while maintaining measurement precision.
Data Source
AI summary
Systems and methods are provided for assigning a quality parameter to a reproductive cellular structure. An image of the reproductive cellular structure is obtained. The image of the reproductive cellular structure is provided to a neural network to generate a value representing a morphology of the reproductive cellular structure. The value is compared to a predefined standard to provide a quality assurance metric representing one of a medical personnel, a facility, a growth medium, and an identity of the reproductive cellular structure.