Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning

STUDY QUESTION: Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)? SUMMARY ANSWER: Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH. WHAT IS KNOWN ALREADY: A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation. STUDY DESIGN, SIZE, DURATION: This study was a retrospective analysis of a dataset of 11,286 cycles performed at a single centre in France between 2009 and 2020 with the aim of building a predictive model for the number of oocytes retrieved from ovarian stimulation. The analysis was carried out by a data analysis team external to the centre using the Substra framework. The Substra framework enabled the data analysis team to send computer code to run securely on the centre's on-premises server. In this way, a high level of data security was achieved as the data analysis team did not have direct access to the data, nor did the data leave the centre at any point during the study. PARTICIPANTS/MATERIALS, SETTING, METHODS: The Light Gradient Boosting Machine algorithm was used to produce three predictive models: one that directly predicted the number of oocytes retrieved and two that predicted which of a set of bins provided by two clinicians the number of oocytes retrieved fell into. The resulting models were evaluated on a held-out test set and compared to linear and logistic regression baselines. In addition, the models themselves were analysed to identify the parameters that had the biggest impact on their predictions. MAIN RESULTS AND THE ROLE OF CHANCE: On average, the model that directly predicted the number of oocytes retrieved deviated from the ground truth by 4.21 oocytes. The model that predicted the first clinician's bins deviated by 0.73 bins whereas the model for the second clinician deviated by 0.62 bins. For all models, performance was best within the first and third quartiles of the target variable, with the model underpredicting extreme values of the target variable (no oocytes and large numbers of oocytes retrieved). Nevertheless, the erroneous predictions made for these extreme cases were still within the vicinity of the true value. Overall, all three models agreed on the importance of each feature which was estimated using Shapley Additive Explanation (SHAP) values. The feature with the highest mean absolute SHAP value (and thus the highest importance) was the antral follicle count, followed by basal AMH and FSH. Of the other hormonal features, basal TSH, LH, and testosterone levels were similarly important and baseline LH was the least important. The treatment characteristic with the highest SHAP value was the initial dose of gonadotropins. LIMITATIONS, REASONS FOR CAUTION: The models produced in this study were trained on a cohort from a single centre. They should thus not be used in clinical practice until trained and evaluated on a larger cohort more representative of the general population. WIDER IMPLICATIONS OF FINDINGS: These predictive models for the number of oocytes retrieved from COH may be useful in clinical practice, assisting clinicians in optimizing COH protocols for individual patients. Our work also demonstrates the promise of using the Substra framework for allowing external researchers to provide clinically relevant insights on sensitive fertility data in a fully secure, trustworthy manner and opens a number of exciting avenues for accelerating future research. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by the French Public Bank of Investment as part of the Healthchain Consortium. T.Fe., C.He., J.C., C.J., C.-A.P., and C.Hi. are employed by Apricity. C.Hi. has received consulting fees and honoraria from Vitrolife, Merck Serono, Ferring, Cooper Surgical, Dibimed, Apricity, and Fairtility and travel support from Fairtility and Vitrolife, participates on an advisory board for Merck Serono, was the founder and organizer of the AI Fertility conference, has stock in Aria Fertility, TMRW, Fairtility, Apricity, and IVF Professionals, and received free equipment from Planar in exchange for first user feedback. C.J. has received a grant from BPI. J.C. has also received a grant from BPI, is a member of the Merck AI advisory board, and is a board member of Labelia Labs. C.He has a contract for medical writing of this manuscript by CHU Nantes and has received travel support from Apricity. A.R. haș received honoraria from Ferring and Organon. T.Fe. has received a grant from BPI. TRIAL REGISTRATION NUMBER: N/A

Assisted reproductive techniques do not impact late neurodevelopmental outcomes of preterm children

ObjectiveAssisted reproductive technology (ART) increases the rate of preterm births, though few studies have analyzed outcomes for these infants. No data are available on 4-year-old children born prematurely after ART. The objective was to investigate whether ART affect the neurodevelopmental outcomes at 4 years in preterm infants born before 34 weeks of gestational age (GA).Methods and resultsA total of 166 ART and 679 naturally conceived preterm infants born before 34 weeks GA between 2013 and 2015 enrolled in the Loire Infant Follow-up Team were included. Neurodevelopment was assessed at 4 years using the age and stage questionnaire (ASQ) and the need for therapy services. The association between the socio-economic and perinatal characteristics and non-optimal neurodevelopment at 4 years was estimated. After adjustment, the ART preterm group remained significantly associated with a lower risk of having at least two domains in difficulty at ASQ: adjusted odds ratio (aOR) 0.34, 95% confidence interval (CI) (0.13–0.88), p = 0.027. The factors independently associated with non-optimal neurodevelopment at 4 years were male gender, low socio-economic level, and 25–30 weeks of GA at birth. The need for therapy services was similar between groups (p = 0.079). The long-term neurodevelopmental outcomes of preterm children born after ART are very similar, or even better than that of the spontaneously conceived children

Anti-müllerian hormone levels and evolution in women of reproductive age with breast cancer treated with chemotherapy

International audienceBackground: Long-term consequences of cancer treatments in young women, and especially fertility issues, are gaining attention as survival rates increase. Breast cancer is the most frequent malignancy in women of reproductive age.Aim: The purpose of this review is to describe serum anti-mü llerian hormone (AMH) level at diagnosis and its evolution during and after chemotherapy in women of reproductive age treated for breast cancer. Second, the impact of taxanes on AMH, the association between AMH and amenorrhea, and the comparison of AMH with other hormonal markers of ovarian reserve were studied.Methods: A systematic PubMed search was conducted on all articles, published up to April 2016 and related to AMH in women suffering from breast cancer using the following key words: AMH, mü llerian-inhibiting substance, ovarian reserve, ovarian function, breast cancer, gonadotoxicity, ovarian toxicity, amenorrhea, chemotherapy, and menopause.Results: AMH levels rapidly fall down to undetectable levels in most women during chemo-therapy and generally persist at very low levels in most women after the treatment. Taxanes seem to impact negatively ovarian function, but data on ovarian reserve are scarce. AMH is a predictor of the occurrence of chemotherapy-related amenorrhea and is the most relevant hormonal marker of ovarian reserve.Conclusion: Serum AMH is a relevant tool for ovarian reserve assessment and follow-up during treatment in premenopausal women with breast cancer. Further large prospective studies are necessary to determine its predictive interest for post-treatment residual fertility, and eventually use it in fertility preservation counseling before treatment initiation

Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study

An important limitation to the development of AI-based solutions for In Vitro Fertilization (IVF) is the black-box nature of most state-of-the-art models, due to the complexity of deep learning architectures, which raises potential bias and fairness issues. The need for interpretable AI has risen not only in the IVF field but also in the deep learning community in general. This has started a trend in literature where authors focus on designing objective metrics to evaluate generic explanation methods. In this paper, we study the behavior of recently proposed objective faithfulness metrics applied to the problem of embryo stage identification. We benchmark attention models and post-hoc methods using metrics and further show empirically that (1) the metrics produce low overall agreement on the model ranking and (2) depending on the metric approach, either post-hoc methods or attention models are favored. We conclude with general remarks about the difficulty of defining faithfulness and the necessity of understanding its relationship with the type of approach that is favored

Metrics for saliency map evaluation of deep learning explanation methods

International audienceDue to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs. Given the high cost of user studies, metrics are necessary to compare and evaluate these different methods. In this paper, we critically analyze the Deletion Area Under Curve (DAUC) and Insertion Area Under Curve (IAUC) metrics proposed by Petsiuk et al. (2018). These metrics were designed to evaluate the faithfulness of saliency maps generated by generic methods such as Grad-CAM or RISE. First, we show that the actual saliency score values given by the saliency map are ignored as only the ranking of the scores is taken into account. This shows that these metrics are insufficient by themselves, as the visual appearance of a saliency map can change significantly without the ranking of the scores being modified. Secondly, we argue that during the computation of DAUC and IAUC, the model is presented with images that are out of the training distribution which might lead to an unreliable behavior of the model being explained. To complement DAUC/IAUC, we propose new metrics that quantify the sparsity and the calibration of explanation methods, two previously unstudied properties. Finally, we give general remarks about the metrics studied in this paper and discuss how to evaluate them in a user study

Metrics for saliency map evaluation of deep learning explanation methods

International audienceDue to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs. Given the high cost of user studies, metrics are necessary to compare and evaluate these different methods. In this paper, we critically analyze the Deletion Area Under Curve (DAUC) and Insertion Area Under Curve (IAUC) metrics proposed by Petsiuk et al. (2018). These metrics were designed to evaluate the faithfulness of saliency maps generated by generic methods such as Grad-CAM or RISE. First, we show that the actual saliency score values given by the saliency map are ignored as only the ranking of the scores is taken into account. This shows that these metrics are insufficient by themselves, as the visual appearance of a saliency map can change significantly without the ranking of the scores being modified. Secondly, we argue that during the computation of DAUC and IAUC, the model is presented with images that are out of the training distribution which might lead to an unreliable behavior of the model being explained. To complement DAUC/IAUC, we propose new metrics that quantify the sparsity and the calibration of explanation methods, two previously unstudied properties. Finally, we give general remarks about the metrics studied in this paper and discuss how to evaluate them in a user study

Synchronous Multimodal Measurements on Lips and Glottis: Comparison Between Two Human-Valve Oscillating Systems

International audienceThe brass instrument-player and the human voice production systems are both composed of a vibrating " human valve " coupled to an acoustic resonator and can be modelled by very similar dynamical systems. Moreover, lips and glottis are both difficult to access during sound production without disturbing their mechanical behaviour and vibration characteristics. In this article, we introduce a common measurement and analysis framework in order to study and compare the vibration of lips and glottis during sound production. Based on previous studies conducted on vibrating vocal folds, our measurement system is composed of three synchronous measurements –electrical admittance (electroglottography and electrolabiography), high-speed video recording and sound recording– and allows relatively non-intrusive measurements to be performed on singers and trombone players. Analysis of the collected data highlights the interpretability of electrolabiographic signals. Furthermore, similarities and differences between the two valve systems are investigated with regard to high speed imaging, electrical admittance and basic characteristics of the radiated sound

Time-lapse in the IVF lab: how should we assess potential benefit?

International audienceComment in Reply: time-lapse in the IVF lab: how should we assess potential benefit? [Hum Reprod. 2015]Comment on Time-lapse in the IVF-lab: how should we assess potential benefit? [Hum Reprod. 2015

Numerical transient hygro-elastic analyses of reinforced Fickian and non-Fickian polymers

International audienceThe present paper is devoted to the impact of water diffusion within reinforced polymer composites. The resulting ageing may damage a structure made with these materials which can no longer perform its function. In order to predict the long-term behavior of such materials in humid environments, numerical investigations can be done with the classical Finite Element Method. In this work, the FEM is used to solve uncoupled mechanical-water diffusion boundary problems. Simulations are done at the microscopic level in order to take into account the heterogeneity of the composite materials. Both the matrix and the reinforcements are assumed linear elastic materials. Two historical models of water diffusion are studied: Fick and Langmuir

Systematic review on clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring

International audienceComment in Reply: Clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring: a systematic review. [Hum Reprod Update. 2015]Comment on Clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring: a systematic review. [Hum Reprod Update. 2014