A method using artificial neural networks to morphologically assess mouse blastocyst quality
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Background: Morphologically classifying embryos is important for numerous laboratory techniques, which rangefrom basic methods to methods for assisted reproduction. However, the standard method currently used forclassification is subjective and depends on an embryologist’s prior training. Thus, our work was aimed atdeveloping software to classify morphological quality for blastocysts based on digital images. Methods: The developed methodology is suitable for the assistance of the embryologist on the task of analyzingblastocysts. The software uses artificial neural network techniques as a machine learning technique. These networks analyzeboth visual variables extracted from an image and biological features for an embryo. Results: After the training process the final accuracy of the system using this method was 95%. To aid theend-users in operating this system, we developed a graphical user interface that can be used to produce a qualityassessment based on a previously trained artificial neural network. Conclusions: This process has a high potential for applicability because it can be adapted to additional specieswith greater economic appeal (human beings and cattle). Based on an objective assessment (without personal biasfrom the embryologist) and with high reproducibility between samples or different clinics and laboratories, thismethod will facilitate such classification in the future as an alternative practice for assessing embryo morphologies.
Abstract
Background: Morphologically classifying embryos is important for numerous laboratory techniques, which rangefrom basic methods to methods for assisted reproduction. However, the standard method currently used forclassification is subjective and depends on an embryologist’s prior training. Thus, our work was aimed atdeveloping software to classify morphological quality for blastocysts based on digital images. Methods: The developed methodology is suitable for the assistance of the embryologist on the task of analyzingblastocysts. The software uses artificial neural network techniques as a machine learning technique. These networks analyzeboth visual variables extracted from an image and biological features for an embryo. Results: After the training process the final accuracy of the system using this method was 95%. To aid theend-users in operating this system, we developed a graphical user interface that can be used to produce a qualityassessment based on a previously trained artificial neural network. Conclusions: This process has a high potential for applicability because it can be adapted to additional specieswith greater economic appeal (human beings and cattle). Based on an objective assessment (without personal biasfrom the embryologist) and with high reproducibility between samples or different clinics and laboratories, thismethod will facilitate such classification in the future as an alternative practice for assessing embryo morphologies.
참고문헌 (20)
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