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An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.

ABSTRACT: BACKGROUND:For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo ploidy status from TLT, artificial intelligence (AI) technology is a good choice. However, the current work of AI in this field needs to be strengthened. METHODS:A total of 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts from April 2018 to November 2019 were included in the study. All embryo images are captured during 5 or 6 days after fertilization before biopsy by time-lapse microscope system. All euploid embryos or aneuploid embryos are used as data sets. The data set is divided into training set, validation set and test set. The training set is mainly used for model training, the validation set is mainly used to adjust the hyperparameters of the model and the preliminary evaluation of the model, and the test set is used to evaluate the generalization ability of the model. For better verification, we used data other than the training data for external verification. A total of 155 PGT cycles from December 2019 to December 2020 and 523 blastocysts were included in the verification process. RESULTS:The euploid prediction algorithm (EPA) was able to predict euploid on the testing dataset with an area under curve (AUC) of 0.80. CONCLUSIONS:The TLT incubator has gradually become the choice of reproductive centers. Our AI model named EPA that can predict embryo ploidy well based on TLT data. We hope that this system can serve all in vitro fertilization and embryo transfer (IVF-ET) patients in the future, allowing embryologists to have more non-invasive aids when selecting the best embryo to transfer.

SUBMITTER: Huang B

PROVIDER: S-EPMC8667440 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThe rate of embryonic aneuploidy increases with increasing female age and is the primary cause of lower pregnancy and live birth rates (LBR) in older reproductive age women. This retrospective cohort study evaluates single euploid embryo transfers to determine whether an age-related decline in reproductive efficiency persists.MethodsA total of 8175 non-donor single embryo transfers (SET) after pre-implantation testing for aneuploidy (PGT-A) and cryopreservation were included. These were divided into five groups by patient age: < 35 years old (n = 3789 embryos transferred), 35-37 (n = 2200), 38-40 (n = 1624), 41-42 (n = 319), and > 42 (n = 243). Implantation rate (IR), clinical pregnancy rate (CPR), and LBR were calculated for each group as a percentage of embryos transferred and compared. CPR was also analyzed as a percentage of implanted pregnancies, and LBR as a percentage of clinical pregnancies, to determine when age has the greatest impact. These results were then adjusted for confounding variables via a multivariate logistic regression model.ResultsImplantation rates negatively correlated with age. After adjusting for confounders, women 38 years or older had a significantly lower IR than those under 35 (OR 0.85, 95%CI 0.73-0.99 for 38-40 years old; 0.69, 0.53-0.91 for 41-42, and 0.69, 0.51-0.94 for > 42). These differences are also apparent in CPR and LBR. The rates of progression to clinical pregnancy and live birth did not differ significantly by age group. Other factors observed to affect IR independently were anti-Müllerian hormone (AMH), day of embryo transfer, and embryo morphology.ConclusionWhile selection of euploid embryos may be effective in overcoming a significant proportion of the age-related decline in reproductive efficiency, a decrease in IR, CPR, and LBR persists even when analyzing only euploid embryo transfers. The observed impact of aging is, therefore, independent of ploidy, as well as of other variables that affect reproductive efficiency. These results indicate that factors other than aneuploidy contribute to reproductive senescence.

Project description:Coronary artery calcium (CAC), which can be measured in various types of computed tomography (CT) examinations, is a hallmark of coronary artery atherosclerosis. However, despite the clinical value of CAC scores in predicting cardiovascular events, routine measurement of CAC scores is limited due to high cost, radiation exposure, and lack of widespread availability. It would be of great clinical significance if CAC could be predicted by electrocardiograms (ECGs), which are cost-effective and routinely performed during various medical checkups. We aimed to develop binary classification artificial intelligence (AI) models that predict CAC using only ECGs as input. Moreover, we aimed to address the generalizability of our model in different environments by externally validating our model on a dataset from a different institution. Among adult patients, standard 12-lead ECGs were extracted if measured within 60 days before or after the CAC scores, and labeled with the corresponding CAC scores. We constructed deep convolutional neural network models based on residual networks using only the raw waveforms of the ECGs as input, predicting CAC at different levels, namely CAC score ≥100, ≥400 and ≥1,000. Our AI models performed well in predicting CAC in the training and internal validation dataset [area under the receiver operating characteristics curve (AUROC) 0.753 ± 0.009, 0.802 ± 0.027, and 0.835 ± 0.024 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively]. Our models also performed well in the external validation dataset (AUROC 0.718, 0.777 and 0.803 for the CAC score ≥100, ≥400, and ≥1,000 model, respectively), indicating that our model can generalize well to different but plausibly related populations. Model performance in terms of AUROC increased in the order of CAC score ≥100, ≥400, and ≥1,000 model, indicating that higher CAC scores might be associated with more prominent structural changes of the heart detected by the model. With our AI models, a substantial proportion of previously unrecognized CAC can be afforded with a risk stratification of CAC, enabling initiation of prophylactic therapy, and reducing the adverse consequences related to ischemic heart disease.

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An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.

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