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. SS protocol required only a one-minute immersion in thaw solution (TS) before the embryos were transferred to culture media. A time-interrupted study was performed evaluating 752 FETs (SW: 376 FETs, SS 376 FETs) between April 2021-December 2022 at a single academic fertility clinic in Boston, Massachusetts. Embryologic, clinical pregnancy, and live birth outcomes were assessed using generalized estimated equation (GEE) models, which accounted for potential confounders. RESULTS: There was 100% survival for all blastocysts (n = 952 embryos) with no differences in blastocyst re-expansion regardless of PGT status. Adjusted analysis showed no differences in implantation, clinical pregnancy, spontaneous abortion, or biochemical pregnancy rate. A higher odds of multiple gestation [AdjOR(95%CI) 1.06 (1.01, 1.11), p = 0.019] were noted, even when adjusting for number of embryos transferred [AdjOR(95%CI) 1.05 (1.01, 1.10)]. Live birth outcomes showed no differences in live birth rates or birthweight at delivery. CONCLUSIONS: The study found equivalent outcomes for SS and SW in all parameters except for a slight rise in the rate of multiple gestations. The results suggest that SS warming is an efficient, viable alternative to SW, reducing thaw times without adverse effects on live birth rates or neonatal birth weights.

Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction.

This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic testing in in vitro fertilization procedures. The current gold standard, preimplantation genetic testing for aneuploidy, has limitations, such as an invasive biopsy, financial burden, delays in results reporting, and difficulty in results reporting, Noninvasive ploidy screening methods, including blastocoel fluid sampling, spent media testing, and AI algorithms using embryonic images and clinical parameters, are explored. Various AI models have been developed using different machine learning algorithms, such as random forest classifier and logistic regression, have shown variable performance in predicting euploidy. Static embryo imaging combined with AI algorithms have demonstrated good accuracy in ploidy prediction, with models such as Embryo Ranking Intelligent Classification Algorithm and STORK-A outperforming human grading. Time-lapse embryo imaging analyzed by AI algorithms has also shown promise in predicting ploidy status; however, the inclusion of clinical parameters is crucial to improving the predictive value of these models. Mosaicism, an important aspect of embryo classification, is often overlooked in AI algorithms and should be considered in future studies. The integration of AI algorithms into microscopy equipment and Embryoscope platforms will facilitate noninvasive genetic testing. Further development of algorithms that optimize clinical considerations and incorporate minimal-necessary covariates will also enhance the predictive value of AI in embryo selection. Artificial intelligence-based ploidy prediction has the potential to improve pregnancy rates and reduce costs in in vitro fertilization cycles.

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, in vitro 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. Cycles were stratified based on the utilization of frozen (FROZEN, n = 1,871) instead of fresh ejaculated sperm (FRESH, n = 3,464). Main outcomes included human chorionic gonadotropin (HCG) positivity, clinical pregnancy (CP), and spontaneous abortion (SAB) rates. Secondary outcome was live birth (LB) rate. Odds ratios (OR) for all outcomes were calculated utilizing logistic regression and adjusted (adjOR) for maternal age, day-3 FSH, and OS regimen. Stratified analysis was performed based on OS subtype [gonadotropins; oral medications (OM): clomiphene citrate and letrozole; and unstimulated/natural]. Time to pregnancy and cumulative pregnancy rates were also calculated. Further subanalyses were performed limited to either the first cycle only or to the partner's sperm only, after excluding female factor infertility, and after stratification by female age (<30, 30-35, and >35 years old). Results: Overall, HCG positivity and CP were lower in the FROZEN compared to the FRESH group (12.2% vs. 15.6%, p < 0.001; 9.4% vs. 13.0%, p < 0.001, respectively), which persisted only among OM cycles after stratification (9.9% vs. 14.2% HCG positivity, p = 0.030; 8.1% vs. 11.8% CP, p = 0.041). Among all cycles, adjOR (95% CI) for HCG positivity and CP were 0.75 (0.56-1.02) and 0.77 (0.57-1.03), respectively, ref: FRESH. In OM cycles, adjOR (95% CI) for HCG positivity [0.55 (0.30-0.99)] and CP [0.49 (0.25-0.95), ref.: FRESH] favored the FRESH group but showed no differences among gonadotropin and natural cycles. SAB odds did not differ between groups among OM and natural cycles but were lower in the FROZEN group among gonadotropin cycles [adjOR (95% CI): 0.13 (0.02-0.98), ref.: FRESH]. There were no differences in CP and SAB in the performed subanalyses (limited to first cycles or partner's sperm only, after excluding female factors, or after stratification according to female age). Nevertheless, time to conception was slightly longer in the FROZEN compared to the FRESH group (3.84 vs. 2.58 cycles, p < 0.001). No significant differences were present in LB and cumulative pregnancy results, other than in the subgroup of natural cycles, where higher LB odds [adjOR (95% CI): 1.08 (1.05-1.12)] and higher cumulative pregnancy rate (34% vs. 15%, p = 0.002) were noted in the FROZEN compared to the FRESH group. Conclusion: Overall, clinical outcomes did not differ significantly between frozen and fresh sperm IUI cycles, although specific subgroups might benefit from fresh sperm utilization.

Artificial intelligence for sperm selection-a systematic review.

Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and pregnancy outcomes. Male infertility is a major contributing factor, and sperm evaluation is a crucial step in diagnosis and treatment. However, embryologists face the daunting task of selecting a single sperm from millions in a sample based on various parameters, which can be time-consuming, subjective, and may even cause damage to the sperm, deeming them unusable for fertility treatments. Artificial intelligence algorithms have revolutionized the field of medicine, particularly in image processing, because of their discerning abilities, efficacy, and reproducibility. Artificial intelligence algorithms have the potential to address the challenges of sperm selection with their large-data processing capabilities and high objectivity. These algorithms could provide valuable assistance to embryologists in sperm analysis and selection. Furthermore, these algorithms could continue to improve over time as larger and more robust datasets become available for their training.

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. PATIENTS: In total, 1,503 women of all diagnoses sought IUI with fresh-ejaculated sperm. EXPOSURE: Cycles were divided into 2 groups on the basis of sperm preparation technique: density gradient centrifugation (n = 1,687, unexposed group) and simple wash (n = 1,691, exposed group). MAIN OUTCOME MEASURES: Primary outcome measures consisted of clinical pregnancy and live birth rates. Furthermore, adjusted odds ratios and 95% confidence intervals for each outcome were calculated and compared between the 2 sperm preparation groups. RESULTS: Odds ratios did not differ between density gradient centrifugation and simple wash groups for clinical pregnancy and live birth (1.10 [0.67-1.83] and 1.08 [0.85-1.37], respectively). Additionally, when cycles were stratified using ovulation induction rather than adjusted for, no differences in clinical pregnancy and live birth odds were noted between sperm preparation groups (gonadotropins: 0.93 [0.49-1.77] and 1.03 [0.75-1.41]; oral agents: 1.78 [0.68-4.61] and 1.05 [0.72-1.53]; unassisted: 0.08 [0.001-6.84] and 2.52 [0.63-10.00], respectively). Furthermore, no difference was seen in clinical pregnancy or live birth when cycles were stratified using sperm score or when the analysis was limited to first cycles only. CONCLUSION: Overall, no difference was noted in clinical pregnancy or live birth rates between patients who received simple wash vs. density gradient-prepared sperm, suggesting similar clinical efficacy between the 2 techniques for IUI. Because the simple wash technique is more time-efficient and cost-effective compared with the density gradient, adoption of this technique could lead to comparable clinical pregnancy and live birth rates for IUI cycles, although optimizing teamwork flow and coordination of care.

Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade.

The integration of artificial intelligence (AI) and deep learning algorithms into medical care has been the focus of development over the last decade, particularly in the field of assisted reproductive technologies and in vitro fertilization (IVF). With embryo morphology the cornerstone of clinical decision making for IVF, the field of IVF is highly reliant on visual assessments that can be prone to error and subjectivity and be dependent on the level of training and expertise of the observing embryologist. Implementing AI algorithms into the IVF laboratory allows for reliable, objective, and timely assessments of both clinical parameters and microscopy images. This review discusses the ever-expanding applications of AI algorithms within the IVF embryology laboratory, aiming to discuss the many advances in multiple aspects of the IVF process. We will discuss how AI will improve various processes and procedures such as assessing oocyte quality, sperm selection, fertilization assessment, embryo assessment, ploidy prediction, embryo transfer selection, cell tracking, embryo witnessing, micromanipulation, and quality management. Overall, AI provides great potential and promise to improve not only clinical outcomes but also laboratory efficiency, a key focus because IVF clinical volume continues to increase nationwide.

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 effect of a model combining AMH and ET on early pregnancy outcomes was a secondary measure. RESULTS: In 52.8% of cycles, ET was last assessed and recorded on the day of hCG administration, while in the remaining 47.2% on the day prior to trigger. In unadjusted regression models, AMH was weakly correlated with ET on hCG trigger day [bAMH (95%CI) = 0.032 (- 0.008, 0.070), p = 0.015]. When adjusting for potential confounders, the positive correlation became significant [0.051 (0.006, 0.102), p = 0.047]. Similar findings were observed when assessing the correlation between AMH and ET on the day prior to hCG trigger. ET was non-significantly associated with the odds of clinical pregnancy, when adjusting for potential confounders, except for when restricting the analysis to couples with idiopathic infertility [OR (95%CI), p-value: 0.787 (0.623, 0.993), 0.044]. CONCLUSION: Our findings support an effect of serum AMH on endometrial development in gonadotropin induced cycles, even when adjusting for the diagnosis of PCOS. ET was not associated with the odds of achieving a clinical pregnancy, except for couples with idiopathic infertility.

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. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom. RESULTS: When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos). CONCLUSIONS: By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.

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. The second model (CNN-AH) was trained (n = 13,908), validated (n = 1908), and tested (n = 3888) to identify the optimal location for AH on the zona pellucida that maximizes distance from healthy blastomeres. RESULTS: The CNN-ICSI model accurately identified the polar body and corresponding optimal ICSI location with 98.9% accuracy (95% CI 98.5-99.2%) with a receiver operator characteristic (ROC) with micro and macro area under the curves (AUC) of 1. The CNN-AH model accurately identified the optimal AH location with 99.41% accuracy (95% CI 99.11-99.62%) with a ROC with micro and macro AUCs of 1. CONCLUSION: Deep CNN models demonstrate powerful potential in accurately identifying key landmarks on oocytes and cleavage stage embryos for micromanipulation. These findings are novel, essential stepping stones in the automation of micromanipulation procedures.

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). METHODS: Pregnancy outcomes from groups of 20 consecutive elective single day 5 blastocyst transfers were evaluated for the following procedures: MD performed ET (N = 160 transfers), embryologist performed ET (N = 160 transfers), embryologist performed EV (N = 160 vitrification procedures), embryologist performed EW (N = 160 warming procedures), and embryologist performed TBx (N = 120 biopsies). AI-generated implantation probabilities for the same embryo cohorts were estimated, as were mean AI-predicted and actual implantation rates for each provider and compared using Wilcoxon singed-rank test. RESULTS: Actual implantation rates following ET performed by one MD provider: "H" was significantly lower than AI-predicted (20% vs. 61%, p = 0.001). Similar results were observed for one embryologist, "H" (30% vs. 60%, p = 0.011). Embryos thawed by embryologist "H" had lower implantation rates compared to AI prediction (25% vs. 60%, p = 0.004). There were no significant differences between actual and AI-predicted implantation rates for EV, TBx, or for the rest of the clinical staff performing ET or EW. CONCLUSIONS: AI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.

A unique placenta previa risk factor profile for pregnancies conceived with assisted reproductive technology.

OBJECTIVE: To define specific risk factors for placenta previa in pregnancies conceived with assisted reproductive technology (ART). DESIGN: Retrospective cohort. SETTING: Fertility centers and inpatient obstetric units in Massachusetts. PATIENT(S): Patients conceiving with ART and delivering at 20 weeks gestation or later between 2011 and 2017 in Massachusetts. INTERVENTION(S): Patient demographic and medical factors and specific components of their cycles. Data were obtained by linking vital records of the State of Massachusetts to reproductive clinic data obtained from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System, and then supplementing this information with laboratory and obstetric data from 2 large academic hospitals. MAIN OUTCOME MEASURE: Associations were tested between multiple cycle- and patient-related factors and placenta previa or low-lying placenta at delivery. After testing for confounders, multivariate models were adjusted for maternal age, history of prior cesarean delivery and birth plurality, and are reported as adjusted relative risks (aRR). RESULT(S): We included 18,939 pregnancies, with 553 (2.9%) having placenta previa at delivery. Advanced maternal age (aRR, 1.25; 95% confidence interval [CI], 1.06-1.48), endometriosis, (aRR, 2.22; 95% CI, 1.71-2.86), and controlled ovarian hyperstimulation (aRR, 1.33; 95% CI, 1.12-1.59) were associated with placenta previa, whereas multiple births (aRR, 0.63; 95% CI, 0.48-0.81) and a history of polycystic ovary syndrome or ovulation disorders (aRR, 0.59; 95% CI, 0.46-0.75) had negative associations. The endometriosis association was strong in nulliparas and the controlled ovarian hyperstimulation association was strong in parous patients in a stratified analysis. No association was seen with prior history of cesarean delivery. CONCLUSION(S): Patients conceiving with ART do not have the typical previa risk factors of prior cesarean delivery and multiple gestations, whereas endometriosis and fresh embryo transfers contributed moderate risk. The embryo transfer process itself may affect previa development in this population.

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. When the algorithm evaluated embryos at a later timepoint on day 3 (~ 70 hpi) and day 5 (~ 110 hpi), it generates another key that was matched with the patient's unique key available in the library. This approach was tested using 400 patient embryo cohorts on day 3 and day 5 and number of correct embryo identifications with the CNN algorithm was measured. RESULTS: CNN technology matched the patient identification within random pools of 8 patient embryo cohorts on day 3 with 100% accuracy (n = 400 patients; 3 replicates). For day 5 embryo cohorts, the accuracy within random pools of 8 patients was 100% (n = 400 patients; 3 replicates). CONCLUSIONS: This study describes an artificial intelligence-based approach for embryo identification. This technology offers a robust witnessing step based on unique morphological features of each embryo. This technology can be integrated with existing imaging systems and laboratory protocols to improve specimen tracking.

The impact of single-step and sequential embryo culture systems on obstetric and perinatal outcomes in singleton pregnancies: the Massachusetts Outcomes Study of Assisted Reproductive Technology.

OBJECTIVE: To compare the obstetric and perinatal outcomes of deliveries conceived with embryos from single-step vs. sequential culture media systems. DESIGN: Historical cohort of Massachusetts vital records linked to assisted reproductive technology clinic data from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System and laboratory embryology data from two large academic hospital fertility centers. SETTING: Not applicable. PATIENTS: Patients with singleton live birth deliveries between 2004 and 2017 conceived with autologous assisted reproductive technology cycles with fresh blastocyst transfer using either single-step (n = 1,058) or sequential (n = 474) culture media systems. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Associations of single-step vs. sequential culture with obstetric outcomes (mode of delivery, placental abnormalities, pregnancy-induced hypertension, and gestational diabetes) and perinatal outcomes (preterm birth, low birthweight, small-for-gestational-age, and large-for-gestational-age [LGA]) were assessed with multivariate logistic modeling, adjusted for maternal age, race/ethnicity, education, parity, insurance type, protein supplementation, oxygen concentration, fertilization method, and number of transferred embryos. RESULTS: Compared with sequential culture, single-step culture was associated with increased odds of LGA (adjusted odds ratio 2.1, 95% confidence interval 1.04-4.22). There were no statistically significant differences between single-step and sequential culture media systems in the odds of placental abnormalities, pregnancy-induced hypertension, gestational diabetes, prematurity, small-for-gestational-age, or low birthweight. CONCLUSIONS: Single-step culture is associated with increased odds of LGA, indicating that embryo culture media systems may affect perinatal outcomes.

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. MATERIALS AND METHODS: This is a retrospective cohort study of 729 IUI treatment cycles (382 patients) performed at an academic fertility center from September 19, 2019 to December 31, 2020. Semen collected at the "clinic" was used for 343 cycles before the Coronavirus Disease 2019 (COVID-19) pandemic (September 19, 2019 to March 21, 2020), and "at-home" collected specimens were used for 386 cycles following revised protocols with COVID-19-driven changes (May 30, 2020 to December 31, 2020). Logistic regression models were performed to evaluate the effect of "at-home" semen collection on achieving a positive pregnancy test (PPT) and a clinical pregnancy (CP). RESULTS: Male and female partners' age, ovarian reserve biomarkers, and stimulation regimens used were similar in the "clinic" and "at-home" groups. In unadjusted models, "at-home" collection had no significant effect on the odds for a PPT [OR (95%CI): 0.733 (0.503-1.069)] or CP [0.816 (0.543-1.226)]. These results persisted even when adjusting for maternal age and anti-Müllerian hormone: PPT [0.739 (0.505-1.081)] and CP [0.826 (0.547-1.248)]. Of the semen analysis parameters under evaluation, only motility appeared to significantly impact the odds of achieving a PPT [1.014 (1.004-1.025)] and a CP [1.017 (1.006-1.029)]. This effect was slightly attenuated for samples collected "at-home" [1.012 (0.997-1.027) and 1.015 (0.999-1.031), respectively, for PPT and CP]. DISCUSSION: This study adds important information to the limited literature regarding the effect of at-home semen collection on IUI outcomes. Under adequate protocols, at-home semen collection should be considered a safe alternative. Additional research is needed to optimize such protocols. CONCLUSION: Our data suggest that at-home semen collection does not negatively impact IUI pregnancy 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. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.

Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Specifically, we used low-quality images acquired using inexpensive portable optical systems to train networks for the evaluation of human embryos, the quantification of human sperm morphology and the diagnosis of malarial infections in the blood, and show that the networks performed well across different data distributions. We also show that adversarial learning can be used with unlabelled data from unseen domain-shifted datasets to adapt pretrained supervised networks to new distributions, even when data from the original distribution are not available. Adaptive adversarial networks may expand the use of validated neural-network models for the evaluation of data collected from multiple imaging systems of varying quality without compromising the knowledge stored in the network.

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. RESULTS: Overall, RT cycles had lower AMH levels compared to no-RT cycles (p < 0.001). This finding persisted when analysis was limited to oral agents but attenuated in FSH cycles. For CC and LET cycles, the predicted probability (PProb) for RT decreased as AMH levels increased (PProb (95%CI): 97% (93-100), 79% (70-88), and 75% (61-89); 85% (78-93), 75% (67-83), and 73% (63-86) for AMH pct.: ≤ 25th, ≥ 50th, and ≥ 75th, for CC and LET, respectively)). However, RT was noted in 98.5% of FSH/IUI cycles regardless of AMH. For CC cycles, those with AMH ≥ 75th pct. had lower odds for RT over cycles with AMH < 75th pct. (OR 0.2, 95%CI 0.04-0.8, p = 0.02). Similarly, lower odds for RT were observed in LET cycles with AMH ≥ 75th pct. (0.6, 0.3-1.4, p = 0.25). CONCLUSION: In PCOS, increasing serum AMH levels are associated with lower probability of RT to oral agents. Our findings constitute a valuable tool for the clinician when counseling PCOS patients and designing a personalized ovulation induction treatment strategy.

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. PATIENT(S): A total of 1,861 gonadotropin stimulation/intrauterine insemination cycles stratified by AMH levels into 3 groups: Low, <25th percentile (<0.7 ng/mL); Middle, ≥25th and <75th percentile (0.7-4.4 ng/mL); and High, ≥75th percentile (≥4.5 ng/mL). INTERVENTION(S): Intrauterine insemination after stimulation with gonadotropins. MAIN OUTCOME MEASURE(S): Cumulative probability of clinical pregnancy over a maximum of 3 and/or 6 cycles and SAB incidence risk rate (IRR). The Kaplan-Meier failure function (log rank test), Cox proportional hazards models, and multilevel mixed-effects Poisson regression models were performed to compare outcomes among the AMH groups. RESULT(S): Overall, in both unadjusted and adjusted models, the probability of achieving a clinical pregnancy was higher in the Middle and High AMH groups compared with that in the Low AMH group, both over 3 (hazard ratios [95% confidence interval], 1.55 [1.05-2.29] and 1.85 [1.22-2.81], respectively) and 6 (1.71 [1.17-2.48] and 2.12 [1.42-3.16], respectively) cycles. In the unadjusted models, the SAB IRR was higher among the Low AMH group (IRR [95% confidence interval], 2.17 (1.11-4.24]), with the relationship persisting after adjusting for age (1.83 [0.93-3.60]). When the SAB IRR were calculated separately for the subpopulations with and without polycystic ovary syndrome, a similar relationship was noted among the latter in the unadjusted (1.94 [0.97-3.88]) and adjusted (1.74 [0.86-3.49]) analyses. CONCLUSION(S): In women undergoing gonadotropin stimulation/intrauterine insemination, AMH appears to affect the probability of achieving a clinical pregnancy. A possible negative impact, independent of age, on the risk of SAB was also suggested.