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. RESULTS: A total of 446 fertility clinic websites were scanned in the search. The embryology team has the least common professional identification by their names (53.58%) compared to gynecology clinicians (96.21%, p < 0.001) and nurses (55.58%, p < 0.001). This trend also applies to other types of professional identifiers, such as headshots and biographies. Professional headshots of embryologists (50.34%) were less prominent than those of gynecology clinicians (93.51%, p < 0.001). A similar trend was observed in the biographies of the embryology team (47.20%) compared to gynecology clinicians (95.08%, p < 0.001). CONCLUSION: The present study revealed that embryologists have low professional visibility on fertility clinic websites. Fertility clinics may prioritize enhancing the online visibility of their embryology laboratory team. This approach could potentially enhance the recognition of their team, foster transparency, and provide accessible information about the skills and expertise of healthcare professionals involved in the treatment process.

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. Likewise, many add-on procedures related to embryology, such as preimplantation genetic testing, are considered "laboratory-developed tests" and are not subject to Food and Drug Administration regulation at present. Should predictive artificial intelligence algorithms in reproduction be considered medical devices or laboratory-developed tests? Certain indications certainly carry a higher risk, such as medication dosage, where the consequences of mismanagement could be severe, whereas others, such as embryo selection, are noninterventional (selecting from a patient's own embryos and the course of treatment does not change) and present little to no risk. The regulatory landscape is complex, involving data diversity and performance, real-world evidence, cybersecurity, and postmarket surveillance.

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. METHODS: The ART Compass (ARTC) digital staff management platform was used to assess the clinical decision-making of ART laboratory staff. The survey modules presented standardized instructions to technologists and measured inter- and intra-technologist variability for subjective "clinical decision-making" type questions. Internal and external comparisons were achieved by providing technologists two answers: (1) a comparison to their own lab director and (2) to the most popular response collectively provided by all lab director level accounts. The platform is hosted on HIPAA compliant Amazon web servers, accessible via web browser and mobile applications for iOS (Apple) and Android mobile devices. RESULTS: Here, we investigated the performance of a digital staff management platform for single embryologist IVF practices and for three IVF lab networks (sites A, B, C) from 2020 to 2022. Embryology dish preparation survey results show variance among respondents in the following: PPE use, media volume, timing of oil overlay, and timing of moving prepared dishes to incubators. Surveying the perceived Gardner score and terms in use for early blastocysts reveals a lack of standardization of terminology and fair to poor agreement. We observed moderate inter-technologist agreement for ICM and TE grade (0.47 and 0.52, respectively). Lastly, the clinical decision of choice to freeze or discard an embryo revealed that agreement to freeze was highest for the top-quality embryos, and that some embryos can be highly contested, evenly split between choice to freeze or discard. CONCLUSIONS: We conclude that a digital platform is a novel and effective tool to automate, routinely monitor, and assure quality for staff-related parameters in ART and IVF laboratories. Use of a digital platform can increase regulatory compliance and provide actionable insight for quality assurance in both single embryologist practices and for large networks. Furthermore, clinical decision-making can be augmented with artificial intelligence integration.

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. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.

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. METHODS: We conducted a systematic review (PRISMA guidelines) to quantify unique output metrics of the SART CORS database. Included were SART member reporting clinics: full-length publications from 2004 to 2021 and conference abstracts from 2015 to 2021, the two key timepoints when the SART CORS database underwent transformative shifts in data collection. RESULTS: We found 206 abstracts presented from 2015 to 2021, 189 full-length peer-reviewed publications since 2004, with 654 unique authors listed on these publications. A total of 19 publications have been highly impactful, garnering over 100 citations at the time of writing. Several higher-order categories, such as endometriosis and tubal infertility, have few publications. The conversion of conference abstracts to full-length papers ranged from 15 to 35% from 2015 to 2021. CONCLUSIONS: A substantial body of literature has been generated by analyzing the SART CORS database. Full-length publications have increased year over year. Some topic areas, such as endometriosis and tubal infertility, may be underrepresented. Conversion of conference abstracts to full-length publications has been low, indicating that more organizational support may be needed to ensure that research is methodologically sound and researchers supported to reach full publication status.

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. The word "war" word infiltrates our vernacular, e.g., the war on poverty, on drugs, on cancer, on COVID, and, apropos, on terror. How did rational approaches to disagreement and conflict evade the world's progress? Reproductive physicians and scientists are dedicated to safeguard lives and build families. Violence is antithetical to our mission as professionals, and moral integrity as humans. We are deeply concerned for, and stand in unity with, our Ukrainian colleagues-the embryologists, scientists, OBGYN and REI physicians, infertility patients, and all people under siege. Reproductive health services for Ukrainians (as with many other war-torn regions) have collapsed. Deeply disturbing reports have emerged that cite civilian hospitals (including maternity centers) being targeted. Liquid nitrogen supplies are scarce. Pregnant mothers and gestational carriers are at emergent risk of delivering in extremely harsh conditions, cold underground bunkers and refugee queues.

Artificial intelligence-the future is now.

The pros and cons of artificial intelligence in assisted reproductive technology are presented.

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.

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. The performance of AI models and their potential clinical utility hinge on the quality and size of the databases used, the types and distribution of data, and the particular AI method applied. Additionally, when images are involved, the method of capturing, preprocessing, and treatment and accurate labeling of images becomes an important component of AI modeling. Inconsistent image treatment or inaccurate labeling of images can lead to an inconsistent database, resulting in poor AI accuracy. We discuss the critical appraisal of AI models in reproductive medicine and convey the importance of transparency and standardization in reporting AI models so that the risk of bias and the potential clinical utility of AI can be assessed.

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?

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. Furthermore, a CNN trained to assess an embryo's implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p<0.0001) from five different fertility centers.

Around one in seven couples have trouble conceiving, which means there is a high demand for solutions such as in vitro fertilization, also known as IVF. This process involves fertilizing and developing embryos in the laboratory and then selecting a few to implant into the womb of the patient. IVF, however, only has a 30% success rate, is expensive and can be both mentally and physically taxing for patients. Selecting the right embryos to implant is therefore extremely important, as this increases the chance of success, minimizes complications and ensures the baby will be healthy. Currently the tools available for making this decision are limited, highly subjective, time-consuming, and often extremely expensive. As a result, embryologists often rely on their experience and observational skills when choosing which embryos to implant, which can lead to a lot of variability. An automated system based on artificial intelligence (AI) could therefore improve IVF success rates by assisting embryologists with this decision and ensuring more consistent results. The AI system could learn how embryos develop over time and then uses this information to select the best embryos to implant from just a single image. This would offer a cheaper alternative to current analysis tools that are only available at the most expensive IVF clinics. Now, Bormann, Kanakasabapathy, Thirumalaraj et al. have developed an AI system for IVF based on thousands of images of embryos. Using individual images, the system selected embryos of a comparable quality to those selected by a human specialist. It also showed a greater ability to identify embryos that will lead to successful implantation. Indeed, the software outperformed 15 embryologists from five different centers across the United States in detecting which embryos were most likely to implant out of a group of high-quality embryos with few visible differences. Artificial intelligence has many potential applications to support expert clinical decision-making. Systems like these could improve success, reduce errors and lead to faster, cheaper and more accessible results. Beyond immediate IVF applications, this system could also be used in research and industry to help understand differences in embryo quality.

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). Our analysis reveals wide variability in how AI and ML methods are described (from not at all or very generic to fully describing the architectural framework) and large variability on accepted dataset sizes (from just 3 patients with 16 follicles in the smallest dataset to 661,060 images of 11,898 human embryos in one of the largest). AI and ML are clearly burgeoning methodologies in human reproduction and embryology and would benefit from early application of reporting standards.