Uterine contractile activity in healthy women throughout the menstrual cycle measured using a novel quantitative two-dimensional transvaginal ultrasound speckle tracking method.

RESEARCH QUESTION: To explore normal uterine contractile function across the menstrual cycle using a novel quantitative ultrasound method. DESIGN: This multicentre prospective observational study took place in three European centres from 2014 to 2022. Uterine contraction frequency (contractions/minute), amplitude, direction (cervix-to-fundus, C2F; fundus-to-cervix; F2C), velocity and coordination were investigated. Features were extracted from transvaginal ultrasound recordings (TVUS) using speckle tracking. Premenopausal women ≥18 years of age, with normal, natural menstrual cycles were included. A normal cycle was defined as: regular (duration 28 ± 2 days), no dysmenorrhoea, no menometrorrhagia. Four-minute TVUS were performed during the menstrual phase, mid-follicular, late follicular phase, early luteal phase and/or late luteal phase. Of the 96 recordings available from 64 women, 70 were suitable for inclusion in the analysis. RESULTS: Contraction frequency (for the posterior wall) and velocity (for the anterior uterine wall in the F2C direction) were highest in the late follicular phase and lowest in the menstrual and late luteal phases (1.61 versus 1.31 and 1.35 contractions/min, P < 0.001 and 0.81 versus 0.67 and 0.62 mm/s, P < 0.001, respectively). No significant difference was found for contraction amplitude. Contraction coordination (simultaneous contraction of the anterior and posterior walls in the same direction) was least coordinated in the mid-follicular phase (P = 0.002). CONCLUSIONS: This is the first study to objectively measure uterine contraction features in healthy women during the natural menstrual cycle on TVUS. Likewise, it introduces contraction coordination as a specific feature of uterine peristalsis. Differences in uterine contractility across the menstrual cycle are confirmed, with highest activity seen in the late follicular phase, and lowest in the late luteal phase.

Characterization of Uterine Peristaltic Waves by Ultrasound Strain Analysis.

Uterine peristalsis (UP) is a wavelike uterine motion that plays an important role in the generation of intrauterine streams for menstrual emptying and to support embryo implantation. Our understanding of uterine mechanical behavior is hampered by a lack of quantitative analysis. Here, we propose a spatiotemporal analysis of UP by ultrasound speckle tracking and dedicated strain analysis. We aim at characterizing UP propagating around the endometrial cavity through the anterior and posterior walls of the uterus. To this end, velocity and coordination features are proposed in this study. We investigated a total of 11 healthy volunteers during their natural menstrual cycle and 81 patients undergoing in vitro fertilization (IVF) treatment. They all received multiple 4-min 2-D transvaginal ultrasound scans. Significant differences in propagation velocity were found among different phases of the menstrual cycle, which are in line with the expected uterine behavior. A significant difference in coordination was found between the group of women with successful (pregnancy at 11 weeks) and unsuccessful IVF. This result suggests that the ability to generate coordinated UP represents an important factor for IVF success. The proposed UP quantification may represent a valuable clinical tool for improved understanding of UP and improved decision-making in the context of IVF procedures.

Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features.

OBJECTIVE: To assess the best-performing machine learning (ML) model and features to predict euploidy in human embryos. DESIGN: Retrospective cohort analysis. SETTING: Department for reproductive medicine in a university hospital. PATIENT(S): One hundred twenty-eight infertile couples treated between January 2016 and December 2019. Demographic and clinical data and embryonic developmental and morphokinetic data from 539 embryos (45% euploid, 55% aneuploid) were analyzed. INTERVENTION(S): Random forest classifier (RFC), scikit-learn gradient boosting classifier, support vector machine, multivariate logistic regression, and naïve Bayes ML models were trained and used in 9 databases containing either 26 morphokinetic features (as absolute [A1] or interim [A2] times or combined [A3]) alone or plus 19 standard development features [B1, B2, and B3] with and without 40 demographic and clinical characteristics [C1, C2, and C3]. Feature selection and model retraining were executed for the best-performing combination of model and dataset. MAIN OUTCOME MEASURE(S): The main outcome measures were overall accuracy, precision, recall or sensitivity, F1 score (the weighted average of precision and recall), and area under the receiver operating characteristic curve (AUC) of ML models for each dataset. The secondary outcome measure was ranking of feature importance for the best-performing combination of model and dataset. RESULT(S): The RFC model had the highest accuracy (71%) and AUC (0.75) when trained and used on dataset C1. The precision, recall or sensitivity, F1 score, and AUC were 66%, 86%, 75%, and 0.75, respectively. The accuracy, recall or sensitivity, and F1 score increased to 72%, 88%, and 76%, respectively, after feature selection and retraining. Morphokinetic features had the highest relative predictive weight. CONCLUSION(S): The RFC model can predict euploidy with an acceptable accuracy (>70%) using a dataset including embryos' morphokinetics and standard embryonic development and subjects' demographic and clinical features.

Assessment of uterine activity during IVF by quantitative ultrasound imaging: a pilot study.

RESEARCH QUESTION: Does uterine activity differ in patients who have undergone successful IVF treatment compared with patients who have undergone unsuccessful IVF treatment? DESIGN: Prospective study of 16 women who underwent fresh single embryo transfer. All patients underwent transvaginal ultrasound in three phases of the IVF treatment: ovarian stimulation 1 h before embryo transfer (ET1) and 5-7 days after embryo transfer (ET5-7). Uterine motion analysis was implemented by a dedicated speckle tracking algorithm; frequency- and amplitude-related features were extracted from the derived signals to characterize the uterine activity in relation to ongoing implantation (positive HCG after 6 weeks) and ongoing pregnancy at 11 weeks. RESULTS: Uterine activity in terms of frequency (ovarian stimulation ET1, P = 0.04; ovarian stimulation ET5-7, P = 0.002) and amplitude (ovarian stimulation ET1, P = 0.0003; ovarian stimulation ET5-7, P = 0.000008) is significantly higher in the ovarian stimulation phase compared with ET1 and ET5-7. Women with ongoing pregnancies showed significantly higher uterine contraction frequency compared with those with no ongoing pregnancies in all phases (ovarian stimulation, P = 0.006; ET1, P = 0.015; ET5-7, P = 0.007). Uterine contraction amplitude was significantly lower (P = 0.037) in women at ET5-7 in women with ongoing pregnancies. CONCLUSIONS: This study is a first step towards assessing uterine activity during IVF objectively and non-invasively. It is an essential step to understanding the previously suggested effect of contractions on IVF failure. Uterine activity after embryo transfer characterized by high frequency and low amplitude may favour embryo implantation. Research with larger patient cohorts is needed to build on current evidence and knowledge of uterine contractions during IVF.

Propagation of spontaneous electrical activity in the ex vivo human uterus.

Contractions of the non-pregnant uterus play a key role in fertility. Yet, the electrophysiology underlying these contractions is poorly understood. In this paper, we investigate the presence of uterine electrical activity and characterize its propagation in unstimulated ex vivo human uteri. Multichannel electrohysterographic measurements were performed in five freshly resected human uteri starting immediately after hysterectomy. Using an electrode grid externally and an electrode array internally, measurements were performed up to 24 h after hysterectomy and compared with control. Up to 2 h after hysterectomy, we measured biopotentials in all included uteri. The median root mean squared (RMS) values of the external measurements ranged between 3.95 µV (interquartile range (IQR) 2.41-14.18 µV) and 39.4 µV (interquartile range (IQR) 10.84-105.64 µV) and were all significantly higher than control (median RMS of 1.69 µV, IQR 1.13-3.11 µV), consisting of chicken breast meat. The RMS values decreased significantly over time. After 24 h, the median RMS (1.27 µV, IQR 0.86-3.04 µV) was comparable with the control (1.69 µV, IQR 1.13-3.11 µV, p = 0.125). The internal measurements showed a comparable pattern over time, but overall lower amplitude. The measured biopotentials propagated over the uterine surface, following both a plane-wave as well as an erratic pattern. No clear pacemaker location nor a preferred propagation direction could be identified. These results show that ex vivo uteri can spontaneously generate propagating biopotentials and provide novel insight contributing to improving our understanding of the electrophysiology of the human non-pregnant uterus.

Impaired implantation in endometriosis compared with couples with male subfertility after transfer of equal quality embryos: a matched cohort study.

RESEARCH QUESTION: Is implantation impaired in patients with endometriosis undergoing IVF and intracytoplasmatic sperm injection (ICSI) cycles? DESIGN: A retrospective matched cohort study was carried out on IVF/ICSI cycles with fresh single embryo transfer at the Department of Assisted Reproductive Medicine, Ghent University Hospital, Belgium, between July 2015 and August 2017 (n = 1053). A total of 118 endometriosis cases were matched 1:1 to 118 couples diagnosed with male subfertility and stratified by embryo quality (identical ALPHA grading categories), female age (±1 year) and parity (±1 delivery). Transvaginal ultrasound, magnetic resonance imaging or laparoscopy was used to diagnosed endometriosis, and the revised American Society for Reproductive Medicine score was used to classify the endometriosis into grade I/II versus grade III/IV. Male subfertility was defined in accordance with World Health Organization criteria (fifth edition). RESULTS: Compared with endometriosis cases, control couples with male subfertility had significantly higher rates of positive HCG test on day 16 (P = 0.047, OR 2.077, CI 1.009 to 4.276), ongoing implantation (defined as a positive fetal heart rate on transvaginal ultrasound at a gestational age of at least 6.5-7 weeks) (P = 0.038, OR 2.265, CI 1.048 to 4.893), ongoing pregnancy (defined by a vital pregnancy at 11 weeks) (P = 0.046, OR 2.292, CI 1.016 to 5.173) and live birth (P = 0.043, OR 2.502, CI 1.029 to 6.087). CONCLUSIONS: After matching for embryo quality, woman's age and parity, rates of positive HCG tests, ongoing implantation, ongoing pregnancy and live birth were more than twice as high in the control group compared with the endometriosis group.

Dedicated Ultrasound Speckle Tracking for Quantitative Analysis of Uterine Motion Outside Pregnancy.

Fertility problems are nowadays being paralleled by important advances in assisted reproductive technologies. Yet the success rate of these technologies remains low. There is evidence that fertilization outcome is affected by uterine motion, but solutions for quantitative analysis of uterine motion are lacking. This work proposes a dedicated method for uterine-motion quantification by B-mode transvaginal ultrasound. Motion analysis is implemented by speckle tracking based on block matching after speckle-size regularization. Sum of absolute differences is the adopted matching metrics. Prior to the analysis, dedicated singular value decomposition (SVD) filtering is implemented to enhance the uterine motion over noise, clutter, and uncorrelated motion induced by neighboring organs and probe movements. SVD and block matching are first optimized by a dedicated ex vivo setup. Robustness to noise and speckle decorrelation is improved by median filtering of the tracking coordinates from surrounding blocks. Speckle tracking is further accelerated by a diamond search. The method feasibility was tested in vivo with a longitudinal study on nine women, aimed at discriminating between four selected phases of the menstrual cycle known to show different uterine behavior. Each woman was scanned in each phase for 4 min; four sites on the uterine fundus were tracked over time to extract strain and distance signals along the longitudinal and transversal directions of the uterus. Several features were extracted from these signals. Among these features, median frequency and contraction frequency showed significant differences between active and quiet phases. These promising results motivate toward an extended validation in the context of fertilization procedures.

Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective.

OBJECTIVE: To develop a random forest model (RFM) to predict implantation potential of a transferred embryo and compare it with a multivariate logistic regression model (MvLRM), based on data from a large cohort including in vitro fertilization (IVF) patients treated with the use of single-embryo transfer (SET) of blastocyst-stage embryos. DESIGN: Retrospective study of a 2-year single-center cohort of women undergoing IVF or intracytoplasmatic sperm injection (ICSI). SETTING: Academic hospital. PATIENT(S): Data from 1,052 women who underwent fresh SET in IVF or ICSI cycles were included. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): The performance of both RFM and MvLRM to predict pregnancy was quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), classification accuracy, specificity, and sensitivity. RESULT(S): ROC analysis resulted in an AUC of 0.74 ± 0.03 for the proposed RFM and 0.66 ± 0.05 for the MvLRM for the prediction of ongoing pregnancies of ≥11 weeks. This RFM approach and the MvLRM yielded, respectively, sensitivities of 0.84 ± 0.07 and 0.66 ± 0.08 and specificities of 0.48 ± 0.07 and 0.58 ± 0.08. CONCLUSION(S): The performance to predict ongoing implantation will significantly improve with the use of an RFM approach compared with MvLRM.

External validation of a prediction model to select the best day-three embryo for transfer in in vitro fertilization or intracytoplasmatic sperm injection procedures.

OBJECTIVE: To evaluate the multivariate embryo selection model by van Loendersloot et al. (2014) (VL) in a different geographical context. DESIGN: This is a retrospective external validation study of a 5-year cohort of women undergoing in vitro fertilization or intracytoplasmatic sperm injection. SETTING: Two outpatient fertility clinics. PATIENT(S): A total of 1,197 women who underwent 1,610 fresh in vitro fertilization or intracytoplasmatic sperm injection cycles with single embryo transfer were included. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): The area under the receiver operating characteristics curve for diagnostic efficacy was used to assess the discriminative value of the model. Calibration for testing the validity of the VL model was performed using the Hosmer-Lemeshow goodness-of-fit test and a calibration plot. RESULT(S): Three hundred thirty-three patients (21%) achieved a viable pregnancy of at least 11 weeks. The area under the receiver operating characteristics curve using the VL model was 0.68. No significant difference between the predicted implantation rate and the observed implantation rates was showed using the Hosmer-Lemeshow (X2= 6.70). The calibration plot showed an intercept of the regression line of 0.34 and the estimated slope was 0.72. CONCLUSION: The investigated VL model was able to distinguish between higher and lower implantation potential of embryos in our clinical setting.

Primum non nocere: earlier cessation of glucose monitoring is possible.

Newborns are at relatively high risk for developing hypoglycaemia in the first 24 h after birth. Well-known risk factors are prematurity, small for gestational age (SGA) or large for gestational age (LGA), and maternal pre-existent or gestational diabetes mellitus. Prolonged hypoglycaemia is associated with poor neurodevelopmental outcomes; hence, prevention through proper monitoring and treatment is important. Given the ongoing debate concerning frequency and duration of screening for neonatal hypoglycaemia, therefore, we investigated the frequency and duration of glucose monitoring safe to discover neonatal hypoglycaemia in different risk groups. Data of newborns at risk for hypoglycaemia were retrospectively collected and analysed. Blood glucose concentrations were measured 1, 3, 6, 12, and 24 h after birth. Moderate hypoglycaemia was defined as a blood glucose concentration of < 2.2 mM and severe hypoglycaemia as a concentration of < 1.5 mM. Of 1570 newborns, 762 (48.5%) had at least one episode of hypoglycaemia in the first 24 h after birth; 30.6% of them had severe hypoglycaemia (all in the first 9 h after birth). Only three SGA and two LGA newborns had a first moderate asymptomatic hypoglycaemic episode beyond 12 h after birth. The incidence of hypoglycaemia increased with accumulation of multiple risk factors. CONCLUSION: Safety of limiting the monitoring to 12 h still has to be carefully evaluated in the presence of SGA or LGA newborns; however, our results suggest that 12 h is enough for late preterm newborns (> 34 weeks) and maternal diabetes. What is Known: • Newborns are at relatively high risk for developing hypoglycaemia and such hypoglycaemia is associated with adverse neurodevelopmental outcomes. • Proper glucose monitoring and prompt treatment in case of neonatal hypoglycaemia are necessary. What is New: • Glucose monitoring 12 h after birth is proficient for most newborns at risk. • Maternal diabetes leads to the highest risk of early neonatal hypoglycaemia and newborns with more than one risk factor are at increased risk of hypoglycaemia.