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Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

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.

SUBMITTER: Chavez-Badiola A

PROVIDER: S-EPMC7064494 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

Chavez-Badiola Alejandro A Flores-Saiffe Farias Adolfo A Mendizabal-Ruiz Gerardo G Garcia-Sanchez Rodolfo R Drakeley Andrew J AJ Garcia-Sandoval Juan Paulo JP

Scientific reports 20200310 1

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 morp ...[more]

PMID: 32157183

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Project description:IntroductionThe global adoption of the "freeze-all strategy" has led to a continuous increase in utilization of single vitrified-warmed blastocyst embryo transfer (SVBT) owing to its clinical effectiveness. Accurate prediction of clinical pregnancy is crucial from a patient-centered perspective. However, this remains challenging, with inherent limitations due to the absence of precise and user-friendly prediction tools. Thus, this study primarily aimed to develop and assess a nomogram based on quantitative clinical data to optimize the efficacy of personalized prognosis assessment.Materials and methodsWe conducted a retrospective cohort analysis of ongoing pregnancy data from 658 patients with infertility who underwent SVBT at our center between October 17, 2017, and December 18, 2021. Patients were randomly assigned to the training (n=461) or validation (n=197) cohort for nomogram development and testing, respectively. A nomogram was constructed using the results of the multivariable logistic regression (MLR), which included clinical covariates that were assessed for their association with ongoing pregnancy.ResultsThe MLR identified eight significant variables that independently predicted ongoing pregnancy outcomes in the study population. These predictors encompassed maternal physiology, including maternal age at oocyte retrieval and serum anti-Müllerian hormone levels; uterine factors, such as adenomyosis; and various embryo assessment parameters, including the number of fertilized embryos, blastocyst morphology, blastulation day, blastocyst re-expansion speed, and presence of embryo string. The area under the receiver operating characteristic curve in our prediction model was 0.675 (95% confidence interval [CI], 0.622-0.729) and 0.656 (95% CI, 0.573-0.739) in the training and validation cohorts, respectively, indicating good discrimination performance in both cohorts.ConclusionsOur individualized nomogram is a practical and user-friendly tool that can provide accurate and useful SVBT information for patients and clinicians. By offering this model to patients, clinical stakeholders can alleviate uncertainty and confusion about fertility treatment options and enhance patients' confidence in making informed decisions.

Project description:The aberrant gene expression in the uterine endometrium and embryo has been the major causes of pregnancy failure in cattle. Therefore, selecting cows having adequate endometrial receptivity and embryos of better developmental competence based on the gene expression could increase the number of calves produced by each cow during its productive life time. We used endometrial and embryo biopsy technology in conjunction with the pregnancy outcome information to establish a direct link between the pre-transfer endometrial or in vivo derived embryo gene expression and pregnancy outcome after embryo transfer. Endometrial samples were collected from Simmental heifers at day 7 and 14 of the estrous cycle, one cycle prior to embryo transfer. In the next cycle, embryo biopsies consisting of 60-70% of inner cell mass and trophectoderm were transferred to the recipients at day 7 of the estrous cycle. The remaining 30-40% parts of the embryos were retained for analysis.Pregnancy diagnosis was performed at days 28 and 42 by ultrasonography and at day 56 by rectal palpation. Those heifers returned to heat at day 21 were considered as non pregnant or non receptive endometrium (NP) while those heifers ended up with successful pregnancy and calf delivery was considered as the calf delivery group or receptive endometrium (CD). Following this, the endometrial samples collected during the pre-transfer period and the embryo biopsies retained during embryo transfer were categorized based on the pregnancy outcome. Those endometrial biopsies collected at days 7 and 14 of the estrous cycle from heifers resulted in successful calf delivery were designated as CDd7 and CDd14, respectively and endometrial biopsies taken at days 7 and 14 of the estrous cycle from those subsequently resulted in no pregnancy were designated as NPd7 and NPd14, respectively. Similarly, the embryo biopsies were classified as those embryo biopsies resulted in successful calf delivery and those resulted in no pregnancy

Dataset Information

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

Publications

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

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