Low E-visibility of embryologists on fertility clinic websites: a web-based cross-sectional study.

Purpose: This study assessed the visibility of embryologists on fertility clinic websites among Society for Assisted Reproductive Technology (SART) and the Human Fertilisation and Embryology Authority (HFEA) member clinics.

Methods: During a 1-month interval (March 2022), all Society for Assisted Reproductive Technology (SART) and the Human Fertilisation and Embryology Authority (HFEA) member fertility clinic websites were evaluated. The professional representation of the primary care team was examined including specialties, the presence of headshots, and biographies.

Unlock the algorithms: regulation of adaptive algorithms in reproduction.

In the USA, the Food and Drug Administration plans to regulate artificial intelligence and machine learning software systems as medical devices to improve the quality, consistency, and transparency of their performance across specific age, racial, and ethnic groups. Embryology procedures do not fall under the federal regulation of "CLIA 88." They are not tests per se; they are cell-based procedures.

Assuring quality in assisted reproduction laboratories: assessing the performance of ART Compass - a digital art staff management platform.

Purpose: Staff management is the most cited ART/IVF laboratory inspection deficiency. Small ART/IVF clinics may be challenged to perform these activities by low staff volume; similarly, large ART/IVF networks may be challenged by high staff volume and large datasets. Here, we sought to investigate the performance of an automated, digital platform solution to manage this necessary task.

New frontiers in embryo selection.

Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility.

SART CORS IVF registry: looking to the past to shape future perspectives.

Purpose: The SART CORS database is an informative source of IVF clinic-specific linked data that provides cumulative live birth rates from medically assisted reproduction in the United States (US). These data are used to develop best practice guidelines, for research, quality assurance, and post-market surveillance of assisted reproductive technologies. Here, we sought to investigate the key areas of current research focus (higher-order categories), discover gaps or underserved areas of ART research, and examine the potential application and impact of newer ART adjuvants, future data collection, and analysis needs.

Protecting life in a time of war.

Despite centuries of lessons from history, war endures. Across Earth, during nearly every year from the beginning of the twentieth century to present day, over 30 wars have been fought resulting in 187 million casualties, excluding the most recent conflict, which is the impetus for this essay (Timeline of 20th and 21st century wars). We are, sadly, a war-mongering people.

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.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence (AI) systems have been proposed for reproductive medicine since 1997. Although AI is the main driver of emergent technologies in reproduction, such as robotics, Big Data, and internet of things, it will continue to be the engine for technological innovation for the foreseeable future. What does the future of AI research look like?

Evaluating predictive models in reproductive medicine.

Predictive modeling has become a distinct subdiscipline of reproductive medicine, and researchers and clinicians are just learning the skills and expertise to evaluate artificial intelligence (AI) studies. Diagnostic tests and model predictions are subject to evaluation. Their use offers potential for both harm and benefit in terms of diagnosis, treatment, and prognosis.

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.

Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018.

Sixteen artificial intelligence (AI) and machine learning (ML) approaches were reported at the 2018 annual congresses of the American Society for Reproductive Biology (9) and European Society for Human Reproduction and Embryology (7). Nearly every aspect of patient care was investigated, including sperm morphology, sperm identification, identification of empty or oocyte containing follicles, predicting embryo cell stages, predicting blastocyst formation from oocytes, assessing human blastocyst quality, predicting live birth from blastocysts, improving embryo selection, and for developing optimal IVF stimulation protocols. This represents a substantial increase in reports over 2017, where just one abstract each was reported at ASRM (AI) and ESHRE (ML).

Actin Cytoskeleton-Mediated Constriction of Membrane Organelles via Endoplasmic Reticulum Scaffolding.

Intracellular organelles constantly undergo fission to facilitate turnover, transport, and functional changes. The cytoskeleton has long been understood to play a role in these events, and recent work strongly suggests that several conserved molecular players cooperate with the cytoskeleton to mediate the fission process. Membrane curvature-inducing, membrane scission proteins, and force-inducing cytoskeletal proteins all cooperate to drive the fission process.

Method for tracking core-contributed publications.

Accurately tracking core-contributed publications is an important and often difficult task. Many core laboratories are supported by programmatic grants (such as Cancer Center Support Grant and Clinical Translational Science Awards) or generate data with instruments funded through S10, Major Research Instrumentation, or other granting mechanisms. Core laboratories provide their research communities with state-of-the-art instrumentation and expertise, elevating research.

hESC derived neuro-epithelial rosettes recapitulate early mammalian neurulation events; an in vitro model.

The in vitro neuralization of hESCs has been widely used to generate central and peripheral nervous system components from neural precursors (Bajpai et al., 2009; Curchoe et al., 2010), most often through an intermediate "rosette" stage.

Early acquisition of neural crest competence during hESCs neuralization.

Background: Neural crest stem cells (NCSCs) are a transient multipotent embryonic cell population that represents a defining characteristic of vertebrates. The neural crest (NC) gives rise to many derivatives including the neurons and glia of the sensory and autonomic ganglia of the peripheral nervous system, enteric neurons and glia, melanocytes, and the cartilaginous, bony and connective tissue of the craniofacial skeleton, cephalic neuroendocrine organs, and some heart vessels.

Methodology/principal Findings: We present evidence that neural crest (NC) competence can be acquired very early when human embryonic stem cells (hESCs) are selectively neuralized towards dorsal neuroepithelium in the absence of feeder cells in fully defined conditions.