Risk of hypertensive disorders of pregnancy in electively induced or expectantly managed full-term IVF pregnancies.

Research Question: Are there differences in the rates of hypertensive disorders of pregnancy among IVF patients who underwent induction of labour at 39 weeks compared with those who were managed expectantly?

Design: This was a retrospective cohort study of 1176 low-risk singleton pregnancies conceived via IVF with no contraindications to vaginal delivery. Two groups were constructed: elective induction of labour from 39 weeks 0 days to 39 weeks 6 days (n = 234); and expectant management (n = 942). The main outcome measure was the incidence of hypertensive disorders of pregnancy.

The impact of clomiphene citrate on the endometrium in comparison to gonadotropins in intrauterine insemination cycles: is it thinner and does it matter?

Objective: To determine whether endometrial thickness (EMT) differs between i) clomiphene citrate (CC) and gonadotropin (Gn) utilizing patients as their own controls, and ii) patients who conceived with CC and those who did not. Furthermore, to investigate the association between late-follicular EMT and pregnancy outcomes, in CC and Gn cycles.

Methods: Retrospective study.

Trophectoderm grade as a predictor of beta human-chorionic gonadotropin rise in early pregnancy.

Purpose: To evaluate the association, if any, between the grade of the trophectoderm (TE) and the rate at which β-human-chorionic gonadotropin (β-HCG) rises in early pregnancy.

Methods: This is a retrospective cohort study including 1116 singleton clinical pregnancies resulting from in vitro fertilization with single day 5 blastocyst transfer at an academic fertility center. TE quality was assessed by trained embryologists employing standard criteria.

Live birth outcomes following single-step blastocyst warming technique - optimizing efficiency without impacting live birth rates.

Purpose: To evaluate the impact of a single-step (SS) warming versus standard warming (SW) protocol on the survival/expansion of vitrified blastocysts and their clinical outcomes post-frozen embryo transfer (FET).

Methods: Retrospective analysis was performed on 200 vitrified/warmed research blastocysts equally divided amongst two thawing protocols utilizing the Fujifilm Warming NX kits (Fujifilm, CA). SW utilized the standard 14-minute manufacturer's guidelines.

The impact of cryopreserved sperm on intrauterine insemination outcomes: is frozen as good as fresh?

Introduction: Frozen sperm utilization might negatively impact cycle outcomes in animals, implicating cryopreservation-induced sperm damage. However, fertilization and intrauterine insemination (IUI) in human studies are inconclusive.

Methods: This study is a retrospective review of 5,335 IUI [± ovarian stimulation (OS)] cycles from a large academic fertility center.

The impact of different sperm preparation methods on clinical pregnancy and live birth rates in intrauterine insemination cycles: a retrospective single-center cohort study.

Objective: To assess the impact of 2 different sperm preparation methods, density gradient centrifugation and simple wash, on clinical pregnancy and live birth rates in intrauterine insemination (IUI) cycles with and without ovulation induction.

Design: Retrospective single-center cohort study.

Setting: Academic fertility center.

Case series of tubo-ovarian abscesses after saline infusion sonohysterography: reconsidering antibiotic prophylaxis.

Saline infusion sonohysterography (SIS) is useful for evaluating intrauterine pathology, particularly for patients with abnormal uterine bleeding and during assessment of infertility. Infectious complications are relatively rare, and tubo-ovarian abscess (TOA) after SIS has only been reported once in the literature. We present two additional cases of TOA after SIS that presented to our institution.

The impact of anti-Müllerian hormone on endometrial thickness in gonadotropin stimulation/intrauterine insemination cycles: is there an effect on pregnancy outcomes?

Purpose: To study the association, if any, between anti-Müllerian hormone (AMH) and pre-ovulatory endometrial thickness (ET) in gonadotropin/intrauterine insemination (IUI) cycles.

Methods: This retrospective cohort study included a total of 964 patients undergoing 1926 gonadotropin/IUI cycles at an academic fertility center. Primary outcome measure was the association between serum AMH and measured ET on the day of and the day before human chorionic gonadotropin hormone (hCG) ovulation trigger.

The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status.

Purpose: To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy.

Methods: A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method.

Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks.

Purpose: To determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and cleavage stage embryo images for micromanipulation procedures such as intracytoplasmic sperm injection (ICSI) or assisted hatching (AH).

Methods: Two convolutional neural network (CNN) models were trained, validated, and tested over three replicates to identify key morphologic landmarks used to guide embryologists when performing micromanipulation procedures. The first model (CNN-ICSI) was trained (n = 13,992), validated (n = 1920), and tested (n = 3900) to identify the optimal location for ICSI through polar body identification.

Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI).

Purpose: Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx).

Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study.

Purpose: To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.

Methods: A CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library.

The effect of semen collection at home on intrauterine insemination outcomes.

Background: The WHO 2010 guidelines recognize at-home semen collection as an acceptable alternative to standard collection at the clinic in "exceptional circumstances." There is lack of sufficient data to determine the need for revisiting these recommendations for treatment purposes.

Objectives: To determine whether at-home semen collection has any effect on intrauterine insemination (IUI) cycle outcomes.

Artificial intelligence in the embryology laboratory: a review.

The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer.

Response to ovulation induction treatments in women with polycystic ovary syndrome as a function of serum anti-Müllerian hormone levels.

Purpose: To assess whether anti-Müllerian hormone (AMH) can predict response to ovulation induction (OI) with clomiphene citrate (CC), letrozole (LET), or follicle-stimulating hormone (FSH) in women with polycystic ovary syndrome (PCOS) undergoing OI/intrauterine inseminations (IUI).

Methods: A total of 738 OI/IUI cycles from 242 patients at an academic center were stratified in three groups by medication: CC (n = 295), LET (n = 180), and FSH (n = 263), in a retrospective fashion. Ovarian response to treatment (RT, development of at least one dominant follicle) was assessed using mixed effects logistic regression models.

Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.

Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture conditions. Contemporary quality control and assurance in the IVF lab can be automated (collect, store, retrieve, and analyze), to elevate quality control and assurance beyond the cursory monthly review. Here we demonstrate that statistical KPI monitoring systems for individual embryologist performance and culture conditions can be detected by artificial intelligence systems to provide systemic, early detection of adverse outcomes, and identify clinically relevant shifts in pregnancy rates, providing critical validation for two statistical process controls proposed in the Vienna Consensus Document; intracytoplasmic sperm injection (ICSI) fertilization rate and day 3 embryo quality.

Pretreatment antimüllerian hormone levels and outcomes of ovarian stimulation with gonadotropins/intrauterine insemination cycles.

Objective: To evaluate the association, if any, between serum antimüllerian hormone (AMH) levels and probability of clinical pregnancy and spontaneous abortion (SAB) in the infertility setting.

Design: Retrospective cohort study.

Setting: Academic fertility center.

Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality.

A critical factor that influences the success of an in-vitro fertilization (IVF) treatment cycle is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories.

A patient-specific model combining antimüllerian hormone and body mass index as a predictor of polycystic ovary syndrome and other oligo-anovulation disorders.

Objective: To determine whether a patient-specific predictive model combining antimüllerian hormone (AMH) levels and body mass index (BMI) can aid in the diagnosis of polycystic ovary syndrome (PCOS) and other ovulatory dysfunction disorders (OVDYS) among infertile women.

Design: Retrospective cohort study.

Setting: Academic fertility center.

Performance of a deep learning based neural network in the selection of human blastocysts for implantation.

Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hr post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available.

Consistency and objectivity of automated embryo assessments using deep neural networks.

Objective: To evaluate the consistency and objectivity of deep neural networks in embryo scoring and making disposition decisions for biopsy and cryopreservation in comparison to grading by highly trained embryologists.

Design: Prospective double-blind study using retrospective data.

Setting: U.

The effect of follicular phase length on cycle outcomes and endometrial development in gonadotrophin ovarian stimulation/intrauterine insemination cycles.

Research Question: Does a shorter follicular phase length (FPL) affect cycle outcomes and endometrial development among women undergoing gonadotrophin ovarian stimulation/intrauterine insemination (OS/IUI)?

Design: Retrospective cohort study of 4773 OS/IUI cycles among 2054 patients. FPL was analysed first continuously, then dichotomously using an arbitrary cut-off at the 15th percentile (8 days) to divide cycles into shorter and longer FPL groups. Receiver operating characteristic (ROC) curves were constructed to further analyse the impact of FPL on all outcomes.

Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.

Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings.

Automated smartphone-based system for measuring sperm viability, DNA fragmentation, and hyaluronic binding assay score.

The fundamental test for male infertility, semen analysis, is mostly a manually performed subjective and time-consuming process and the use of automated systems has been cost prohibitive. We have previously developed an inexpensive smartphone-based system for at-home male infertility screening through automatic and rapid measurement of sperm concentration and motility. Here, we assessed the feasibility of using a similar smartphone-based system for laboratory use in measuring: a) Hyaluronan Binding Assay (HBA) score, a quantitative score describing the sperm maturity and fertilization potential in a semen sample, b) sperm viability, which assesses sperm membrane integrity, and c) sperm DNA fragmentation that assesses the degree of DNA damage.