Objective way to support embryo transfer: a probabilistic decision

STUDY QUESTION Is it feasible to identify factors that significantly affect the clinical outcome of IVF-ICSI cycles and use them to reliably design a predictor of implantation? SUMMARY ANSWER The Bayesian network (BN) identified top-history embryos, female age and the insemination technique as the most relevant factors for predicting the occurrence of pregnancy (AUC, area under curve, of 0.72). In addition, it could discriminate between no implantation and single or twin implantations in a prognostic model that can be used prospectively. WHAT IS KNOWN ALREADY The key requirement for achieving a single live birth in an IVF-ICSI cycle is the capacity to estimate embryo viability in relation to maternal receptivity. Nevertheless, the lack of a strong predictor imposes several restrictions on this strategy. STUDY DESIGN, SIZE, DURATION Medical histories, laboratory data and clinical outcomes of all fresh transfer cycles performed at the International Institute for Reproductive Medicine of Lugano, Switzerland, in the period 2006-2008 (n = 388 cycles), were retrospectively evaluated and analyzed. PARTICIPANTS/MATERIALS, SETTING, METHODS Patients were unselected for age, sperm parameters or other infertility criteria. Before being admitted to treatment, uterine anomalies were excluded by diagnostic hysteroscopy. To evaluate the factors possibly related to embryo viability and maternal receptivity, the class variable was categorized as pregnancy versus no pregnancy and the features included: female age, number of previous cycles, insemination technique, sperm of proven fertility, the number of transferred top-history embryos, the number of transferred top-quality embryos, the number of follicles >14 mm and the level of estradiol on the day of HCG administration. To assess the classifier, the indicators of performance were computed by cross-validation. Two statistical models were used: the decision tree and the BN. MAIN RESULTS AND THE ROLE OF CHOICE The decision tree identified the number of transferred top-history embryos, female age and the insemination technique as the features discriminating between pregnancy and no pregnancy. The model achieved an accuracy of 81.5% that was significantly higher in comparison with the trivial classifier, but the increase was so modest that the model was clinically useless for predictions of pregnancy. The BN could more reliably predict the occurrence of pregnancy with an AUC of 0.72, and confirmed the importance of top-history embryos, female age and insemination technique in determining implantation. In addition, it could discriminate between no implantation, single implantation and twin implantation with the AUC of 0.72, 0.64 and 0.83, respectively. LIMITATIONS, REASONS FOR CAUTION The relatively small sample of the study did not permit the inclusion of more features that could also have a role in determining the clinical outcome. The design of this study was retrospective to identify the relevant features; a prospective study is now needed to verify the validity of the model. WIDER IMPLICATIONS OF THE FINDINGS The resulting predictive model can discriminate with reasonable reliability between pregnancy and no pregnancy, and can also predict the occurrence of a single pregnancy or multiple pregnancy. This could represent an effective support for deciding how many embryos and which embryos to transfer for each couple. Due to its flexibility, the number of variables in the predictor can easily be increased to include other features that may affect implantation. STUDY FUNDING/COMPETING INTERESTS This study was supported by a grant, CTI Medtech Project Number: 9707.1 PFLS-L, Swiss Confederation. No competing interests are declare

Deep Learning to Predicting Live Births and Aneuploid Miscarriages from Images of Blastocysts Combined with Maternal Age

Objectives: Making an artificial intelligence (AI) classifier that uses the maternal age and an image of the implanted blastocyst to determine the probability of getting a live birth. Methods: The dataset comprised maternal age data and 407 images of blastocysts which led to live births and 246 images of blastocysts which led to aneuploid miscarriages, matched for maternal age. An AI system using deep learning was developed for predicting the classification and probability of a live birth. Results: The accuracy, sensitivity, specificity, and positive and negative predictive values of the developed AI classifier were 0.75, 0.82, 0.64, 0.79, and 0.68, respectively. The area under the curve was 0.73 ± 0.04 (mean ± standard error). Conclusions: A classifier using AI for a blastocyst image combined with the maternal age showed potential in determining the probability of a live birth

Exploring the complexities of AI-Assisted Embryo Selection: a comprehensive review

In-Vitro Fertilization is among the most widespread and successful treatments for infertility. One of its main challenges is the evaluation and selection of embryos for implantation, a process which suffers from large inter- and intra-observer variability. Due to the advancements in time-lapse imaging, Deep Learning (DL) methods are gaining attention to address this issue, raising both technical and ethical questions. The published works on the topic either fail to address the generality of the problem by focusing on a particular approach or compare different approaches in a misleading manner. In this master thesis, we present and compare different DL-based alternatives delving into technical characteristics, explainability aspects, ethical considerations and clinical applications. Moreover, we propose a set of guidelines for the development of an AI model for embryo selection based on the previous analysis of the literature. Our ultimate goal is to offer a better understanding of the complexities involved in this problem as a necessary first step while working in such a sensitive domain

Fitting the data from embryo implantation prediction: Learning from label proportions

Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve machine learning techniques for embryo selection. In this study, a dataset of 330 consecutive cycles (and associated embryos) carried out by the Unit of Assisted Reproduction of the Hospital Donostia (Spain) throughout 18 months has been analyzed. The problem of the embryo selection has been modeled by a novel weakly supervised paradigm, learning from label proportions, which considers all the available data, including embryos whose fate cannot be certainly established. Furthermore, all the collected features, describing cycles and embryos, have been considered in a multi-variate data analysis. Our integral solution has been successfully tested. Experimental results show that the proposed technique consistently outperforms an equivalent approach based on standard supervised classification. Embryos in this study were selected for transference according to the criteria of the Spanish Association for Reproduction Biology Studies. Obtained classification models outperform these criteria, specifically reordering medium-quality embryos

Quantitative approaches in clinical reproductive endocrinology

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes

Predicting the outcomes of assisted reproductive technology treatments : a systematic review and quality assessment of prediction models

Objective Predicting the outcomes of assisted reproductive technology (ART) treatments is desirable, but adopting prediction models into clinical practice remains limited. We aimed to review available prediction models for ART treatments by conducting a systematic review of the literature to identify the best performing models for their accuracy, generalisability and applicability. Evidence review We searched electronic databases (MEDLINE, EMBASE, and CENTRAL) until June 2020. We included studies reporting on the development or evaluation of models predicting the reproductive outcomes before (pre-ART) or after starting (Intra-ART) treatment in couples undergoing any ART treatment. We evaluated the models’ discrimination, calibration, type of validation, and any implementation tools for clinical practice. Results We included 69 cohort studies reporting on 120 unique prediction models. Half the studies reported on pre-ART (48%) and half on intra-ART (56%) prediction models. The commonest predictors used were maternal age (90%), tubal factor subfertility (50%), and embryo quality (60%). Only fourteen models were externally-validated (14/120, 12%) including eight pre-ART models (Templeton, Nelson, LaMarca, McLernon, Arvis, and the Stolwijk A/I,C,II models), and five intra-ART models (Cai, Hunault, van Loendersloot, Meijerink, Stolwijk B, and the McLernon post-treatment model) with a reported c-statistics ranging from 0.50 to 0.78. Ten of these models provided implementation tools for clinical practice with only two reported online calculators. Conclusion We identified externally validated prediction models that could be used to advise couples undergoing ART treatments on their reproductive outcomes. The quality of available models remains limited and more research is needed to improve their generalizability and applicability into clinical practice

Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records

The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /- 0.01%. A parsimonious predictive model utilizing age (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /- 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.IIS-1914792 - National Science Foundation; N00014-19-1-2571 - Office of Naval Research Global; GM135930 - Office of Extramural Research, National Institutes of HealthPublished versio

A matter of timing : A modelling-based investigation of the dynamic behaviour of reproductive hormones in girls and women

Hypothalamus-hypofyse-gonade aksen er en del av det kvinnelige endokrine systemet, og regulerer evnen til reproduksjon. Hormoner produsert og utskilt fra tre kjertler (hypotalamus, hypofysen, eggstokkene) påvirker hverandre via tilbakemeldingsinteraksjoner, som er nødvendige for å etablere en regelmessig menstruasjonssyklus hos kvinner. Matematiske modeller som forutsier utviklingen av slike hormonkonsentrasjoner og modning av eggstokkfollikler er nyttige verktøy for å forstå menstruasjonssyklusens dynamiske oppførsel. Slike modeller kan for eksempel hjelpe oss med å undersøke patologiske tilstander som endometriose og polycystisk ovariesyndrom. Videre kan de brukes til systematiske undersøkelser av effekten av medikamenter på det kvinnelige endokrine systemet. Derfor kan vi potensielt bruke slike menstruasjonsyklusmodeller som kliniske beslutningsstøttessystemer. Vi trenger modeller som forutsier hormonkonsentrasjoner sammen med modningen av eggstokkfollikler hos enkeltindivider gjennom påfølgende sykluser. Dette for å kunne simulere hormonelle behandlinger som stimulerer vekst av eggstokkfolliklene (eggstokkstimuleringsprotokoller). Her legger jeg fram et forslag til en matematisk menstruasjonsyklusmodell og viser modellens evne til å forutsi resultatet av eggstokkstimuleringsprotokoller. For å kalibrere denne typen modell trenges individuelle tidsseriedata. Innsamling av slike data er tidskrevende, og forutsetter høy grad av engasjement fra deltakerne i studien. Det er derfor viktig å finne brukbare datatyper som er mindre tid- og ressurskrevende å samle inn, og som likevel kan brukes til modellkalibrering. En type data som er enklere å samle inn er tversnittdata. I denne avhandlingen har jeg utviklet en prosedyre for å bruke tversnittpopulasjonsdata i modellens kalibreringsprosess, og viser hvordan en modell kalibrert med tversnittdata kan brukes til å forutsi individuelle resultater ved oppdatering av en del av modellens parametere. I tillegg til det vitenskapelige bidraget, håper jeg at avhandlingen min skaper oppmerksomhet rundt viktigheten av forskning på kvinners reproduktive helse, og at avhandlingen underbygger verdien av matematiske modeller i forskning på kvinnehelse.The hypothalamic-pituitary-gonadal axis (HPG axis), a part of the human endocrine system, regulates the female reproductive function. Feedback interactions between hormones secreted from the glands forming the HPG axis are essential for establishing a regular menstrual cycle. Mathematical models predicting the time evolution of hormone concentrations and the maturation of ovarian follicles are useful tools for understanding the dynamic behaviour of the menstrual cycle. Such models can, for example, help us to investigate pathological conditions, such as endometriosis or Polycystic Ovary Syndrome. Furthermore, they can be used to systematically study the effects of drugs on the endocrine system. In doing so, menstrual cycle models could potentially be integrated into clinical routines as clinical decision support systems. For the simulation-based investigation of hormonal treatments aiming to stimulate the growth of ovarian follicles (Controlled Ovarian Stimulation (COS)), we need models that predict hormone concentrations and the maturation of ovarian follicles in biological units throughout consecutive cycles. Here, I propose such a mechanistic menstrual cycle model. I also demonstrate its capability to predict the outcome of COS. Individual time series data is usually used to calibrate mechanistic models having clinical implications. Collecting these data, however, is time-consuming and requires a high commitment from study participants. Therefore, integrating different data sets into the model calibration process is of interest. One type of data that is often more feasible to collect than individual time series is cross-sectional data. As part of my thesis, I developed a workflow based on Bayesian updating to integrate cross-sectional data into the model calibration process. I demonstrate the workflow using a mechanistic model describing the time evolution of reproductive hormones during puberty in girls. Exemplary, I show that a model calibrated with cross-sectional data can be used to predict individual dynamics after updating a subset of model parameters. In addition to the scientific contributions of this thesis, I hope that it creates attention for the importance of research in the area of women's reproductive health and underpins the value of mathematical modelling for this field.Doktorgradsavhandlin