Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics

Yang, Liubin; Peavey, Mary; Kaskar, K.; Chappell, Neil; Zhu, Lynn; Devlin, Darius J; Valdés, Cecilia T.; Schutt, Amy K.; Woodard, Terri L.; Zarutskie, P.W.; Cochran, R.; Gibbons, William E.

doi:10.1016/j.xfre.2022.04.004

Cited by 6 publications

(4 citation statements)

References 58 publications

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“…Current decision support tools for assessing the implantation outcome utilize various machine learning models that are based on the morphokinetic profiles of the embryos as an input 3,[18][19][20] . Here we employed our dataset consisting of 2497 day-3 transferred embryos labeled by known implantation outcome to train a CatBoost model (Methods).…”

Section: Designing An Arrest-forecasting Policy For Improving Implant...mentioning

confidence: 99%

Improving the assessment of embryo developmental potential via morphokinetic forecasting of future events using language modeling

Zabari,

Kan-Tor,

Srebnik

et al. 2023

Preprint

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In IVF treatments, accurate assessment of the developmental potential of embryos to implant is essential for reaching reasonable pregnancy rates while shortening time-to-pregnancy. Hence, clinical guidelines recommend extended incubation to blastocyst transfers, which provide better evaluation of embryo developmental potential. However, cleavage stage transfer is often favored owing to various clinical considerations. To improve embryo assessment of cleavage stage embryos without extended incubation, we present a computational strategy for forecasting future morphokinetic events. Motivated by the advances in language modeling, we adapt generative pre-training to forecast future morphokinetic events based on the sequence of present events. We demonstrate < 12% forecasting error in forecasting up to three consecutive events. A new policy is proposed that combines morphokinetic forecasting and assessment of the risk of embryo developmental arrest. Using this policy, we demonstrate an improvement in the prediction of known implantation outcome of day-3 embryos from AUC 0.667 to 0.707. We expect morphokinetic forecasting to address the inherent hurdles in the selection of cleavage-stage embryos for transfer. In addition, we hope that demonstrating for the first time the utilization of language modeling on non-textual data in healthcare will stimulate future applications in reproductive medicine and other disciplines.

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Section: Designing An Arrest-forecasting Policy For Improving Implant...mentioning

confidence: 99%

Improving the assessment of embryo developmental potential via morphokinetic forecasting of future events using language modeling

Zabari,

Kan-Tor,

Srebnik

et al. 2023

Preprint

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“…Tailored medical interventions and personalized health management recommendations can then be established for each patient. Early detection through such predictive models provides a valuable opportunity for treatment, facilitating interventions such as targeted lifestyle modifications (e.g., diet and exercise programs) and stress management strategies [15]. By adopting these proactive measures, individuals identified as high-risk can significantly reduce their CVD risk and take greater control of their overall well-being.…”

Section: Introductionmentioning

confidence: 99%

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

Lin,

Lin

2024

Mathematics

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The timely and precise prediction of cardiovascular disease (CVD) risk is essential for effective prevention and intervention. This study proposes a novel framework that integrates the two-phase Taguchi method (TPTM), the hyperparameter artificial neural network (HANN), and a genetic algorithm (GA) called TPTM-HANN-GA. This framework efficiently optimizes hyperparameters for an artificial neural network (ANN) model during the training stage, significantly enhancing prediction accuracy for cardiovascular disease (CVD) risk. The proposed TPTM-HANN-GA framework requires far fewer experiments than a traditional grid search, making it highly suitable for application in resource-constrained, low-power computers, and edge artificial intelligence (edge AI) devices. Furthermore, the proposed TPTM-HANN-GA framework successfully identified the optimal configurations for the ANN model’s hyperparameters, resulting in a hidden layer of 4 nodes, a tan h activation function, an SGD optimizer, a learning rate of 0.23425849, a momentum rate of 0.75462782, and seven hidden nodes. This optimized ANN model achieves 74.25% accuracy in predicting the risk of cardiovascular disease, which exceeds the existing state-of-the-art GA-ANN and TSTO-ANN models. The proposed TPTM-HANN-GA framework enables personalized CVD prediction to be efficiently conducted on low-power computers and edge-AI devices, achieving the goal of point-of-care testing (POCT) and empowering individuals to manage their heart health effectively.

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“…While additional groups have described deep learning algorithms trained on videos of developing embryos ( Sawada et al , 2021 ; Yang et al , 2022 ), none seem to have combined them with multiple variables describing the characteristics of the patient or the cycle. This is necessary to adjust the chances an embryo has to lead to a pregnancy, in the context, for example, of endometrial receptivity as it is known the interactions between the embryo and the endometrium play an important role in embryo implantation ( Lessey and Young, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems

Duval¹,

Nogueira

Dissler³

et al. 2023

Human Reproduction

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STUDY QUESTION Can artificial intelligence (AI) algorithms developed to assist embryologists in evaluating embryo morphokinetics be enriched with multi-centric clinical data to better predict clinical pregnancy outcome? SUMMARY ANSWER Training algorithms on multi-centric clinical data significantly increased AUC compared to algorithms that only analyzed the time-lapse system (TLS) videos. WHAT IS KNOWN ALREADY Several AI-based algorithms have been developed to predict pregnancy, most of them based only on analysis of the time-lapse recording of embryo development. It remains unclear, however, whether considering numerous clinical features can improve the predictive performances of time-lapse based embryo evaluation. STUDY DESIGN, SIZE, DURATION A dataset of 9986 embryos (95.60% known clinical pregnancy outcome, 32.47% frozen transfers) from 5226 patients from 14 European fertility centers (in two countries) recorded with three different TLS was used to train and validate the algorithms. A total of 31 clinical factors were collected. A separate test set (447 videos) was used to compare performances between embryologists and the algorithm. PARTICIPANTS/MATERIALS, SETTING, METHODS Clinical pregnancy (defined as a pregnancy leading to a fetal heartbeat) outcome was first predicted using a 3D convolutional neural network that analyzed videos of the embryonic development up to 2 or 3 days of development (33% of the database) or up to 5 or 6 days of development (67% of the database). The output video score was then fed as input alongside clinical features to a gradient boosting algorithm that generated a second score corresponding to the hybrid model. AUC was computed across 7-fold of the validation dataset for both models. These predictions were compared to those of 13 senior embryologists made on the test dataset. MAIN RESULTS AND THE ROLE OF CHANCE The average AUC of the hybrid model across all 7-fold was significantly higher than that of the video model (0.727 versus 0.684, respectively, P = 0.015; Wilcoxon test). A SHapley Additive exPlanations (SHAP) analysis of the hybrid model showed that the six first most important features to predict pregnancy were morphokinetics of the embryo (video score), oocyte age, total gonadotrophin dose intake, number of embryos generated, number of oocytes retrieved, and endometrium thickness. The hybrid model was shown to be superior to embryologists with respect to different metrics, including the balanced accuracy (P ≤ 0.003; Wilcoxon test). The likelihood of pregnancy was linearly linked to the hybrid score, with increasing odds ratio (maximum P-value = 0.001), demonstrating the ranking capacity of the model. Training individual hybrid models did not improve predictive performance. A clinic hold-out experiment was conducted and resulted in AUCs ranging between 0.63 and 0.73. Performance of the hybrid model did not vary between TLS or between subgroups of embryos transferred at different days of embryonic development. The hybrid model did fare better for patients older than 35 years (P < 0.001; Mann–Whitney test), and for fresh transfers (P < 0.001; Mann–Whitney test). LIMITATIONS, REASONS FOR CAUTION Participant centers were located in two countries, thus limiting the generalization of our conclusion to wider subpopulations of patients. Not all clinical features were available for all embryos, thus limiting the performances of the hybrid model in some instances. WIDER IMPLICATIONS OF THE FINDINGS Our study suggests that considering clinical data improves pregnancy predictive performances and that there is no need to retrain algorithms at the clinic level unless they follow strikingly different practices. This study characterizes a versatile AI algorithm with similar performance on different time-lapse microscopes and on embryos transferred at different development stages. It can also help with patients of different ages and protocols used but with varying performances, presumably because the task of predicting fetal heartbeat becomes more or less hard depending on the clinical context. This AI model can be made widely available and can help embryologists in a wide range of clinical scenarios to standardize their practices. STUDY FUNDING/COMPETING INTEREST(S) Funding for the study was provided by ImVitro with grant funding received in part from BPIFrance (Bourse French Tech Emergence (DOS0106572/00), Paris Innovation Amorçage (DOS0132841/00), and Aide au Développement DeepTech (DOS0152872/00)). A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. and F.D.M. hold a patent for ‘Devices and processes for machine learning prediction of in vitro fertilization’ (EP20305914.2). A.D., N.D., M.M.F., and F.D.M. are or have been employees of ImVitro and have been granted stock options. X.P.-V. has been paid as a consultant to ImVitro and has been granted stocks options of ImVitro. L.C.-D. and C.G.-S. have undertaken paid consultancy for ImVitro SAS. The remaining authors have no conflicts to declare. TRIAL REGISTRATION NUMBER N/A.

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Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics

Cited by 6 publications

References 58 publications

Improving the assessment of embryo developmental potential via morphokinetic forecasting of future events using language modeling

Improving the assessment of embryo developmental potential via morphokinetic forecasting of future events using language modeling

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems

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