Abstract
Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221). We created a system consisting of different classifiers that is feed with novel morphometric features extracted from the digital micrographs, along with other non-morphometric data to predict pregnancy. It was evaluated using five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cross validation to assess the model’s generalization capabilities. In the database A, the SVM classifier achieved an F1 score of 0.74, and AUC of 0.77. In the database B the RF classifier obtained a F1 score of 0.71, and AUC of 0.75. Our results suggest that the system is able to predict a positive pregnancy test from a single digital image, offering a novel approach with the advantages of using a small database, being highly adaptable to different laboratory settings, and easy integration into clinical practice.
Highlights
Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays
Based on the F1 score, the Support Vector Machines (SVM) was the model that obtained the best performance in dbA and ALL databases, while Random Forest performed better in the dbB data
The results in this study suggest software prototype AIR E, can successfully extract features from individual blastocyst micrographs to feed its embedded algorithm and predict beta human chorionic gonadotropin (b-hCG) test results
Summary
Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. The quantitative evaluation of embryo characteristics with digital imaging systems has become an active research area[9,10], and efforts have been made to improve objectivity during the embryo selection process by combining multiple morphological parameters and morphokinetic analysis in mathematical models, aiming to generate predictive capabilities for development potential and implantation. Such models, are often limited to initial development stages of the embryo, dependent on currently existing classifications, or require sophisticated time-lapse microscopy systems[11,12,13,14].
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