Seeking arrangements: cell contact as a cleavage-stage biomarker.

RESEARCH QUESTION: What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos? DESIGN: This pilot study was a retrospective analysis of two Embryoscope imaging datasets from two clinics. An artificial intelligence system was used to reconstruct the three-dimensional structure of embryos from 11-plane focal stacks. Networks of cell contacts were extracted from the resulting embryo three-dimensional models and each embryo's mean contacts per cell was computed. Unpaired t-tests and receiver operating characteristic curve analysis were used to statistically analyse mean cell contact outcomes. Cell contact networks from different embryos were compared with identical embryos with similar cell arrangements. RESULTS: At t4, a higher mean number of contacts per cell was associated with greater rates of blastulation and blastocyst quality. No associations were found with biochemical pregnancy, live birth, miscarriage or ploidy. At t8, a higher mean number of contacts was associated with increased blastocyst quality, biochemical pregnancy and live birth. No associations were found with miscarriage or aneuploidy. Mean contacts at t4 weakly correlated with those at t8. Four-cell embryos fell into nine distinct cell arrangements; the five most common accounted for 97% of embryos. Eight-cell embryos, however, displayed a greater degree of variation with 59 distinct cell arrangements. CONCLUSIONS: Evidence is provided for the clinical relevance of cleavage-stage cell arrangement in the human preimplantation embryo beyond the four-cell stage, which may improve selection techniques for day-3 transfers. This pilot study provides a strong case for further investigation into spatial biomarkers and three-dimensional morphokinetics.

Embryo ranking agreement between embryologists and artificial intelligence algorithms.

OBJECTIVE: To evaluate the degree of agreement of embryo ranking between embryologists and eight artificial intelligence (AI) algorithms. DESIGN: Retrospective study. PATIENT(S): A total of 100 cycles with at least eight embryos were selected from the Weill Cornell Medicine database. For each embryo, the full-length time-lapse (TL) videos, as well as a single embryo image at 120 hours, were given to five embryologists and eight AI algorithms for ranking. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Kendall rank correlation coefficient (Kendall's τ). RESULT(S): Embryologists had a high degree of agreement in the overall ranking of 100 cycles with an average Kendall's tau (K-τ) of 0.70, slightly lower than the interembryologist agreement when using a single image or video (average K-τ = 0.78). Overall agreement between embryologists and the AI algorithms was significantly lower (average K-τ = 0.53) and similar to the observed low inter-AI algorithm agreement (average K-τ = 0.47). Notably, two of the eight algorithms had a very low agreement with other ranking methodologies (average K-τ = 0.05) and between each other (K-τ = 0.01). The average agreement in selecting the best-quality embryo (1/8 in 100 cycles with an expected agreement by random chance of 12.5%; confidence interval [CI]95: 6%-19%) was 59.5% among embryologists and 40.3% for six AI algorithms. The incidence of the agreement for the two algorithms with the low overall agreement was 11.7%. Agreement on selecting the same top two embryos/cycle (expected agreement by random chance corresponds to 25.0%; CI95: 17%-32%) was 73.5% among embryologists and 56.0% among AI methods excluding two discordant algorithms, which had an average agreement of 24.4%, the expected range of agreement by random chance. Intraembryologist ranking agreement (single image vs. video) was 71.7% and 77.8% for single and top two embryos, respectively. Analysis of average raw scores indicated that cycles with low diversity of embryo quality generally resulted in a lower overall agreement between the methods (embryologists and AI models). CONCLUSION(S): To our knowledge, this is the first study that evaluates the level of agreement in ranking embryo quality between different AI algorithms and embryologists. The different concordance methods were consistent and indicated that the highest agreement was intraembryologist agreement, followed by interembryologist agreement. In contrast, the agreement between some of the AI algorithms and embryologists was similar to the inter-AI algorithm agreement, which also showed a wide range of pairwise concordance. Specifically, two AI models showed intra- and interagreement at the level expected from random selection.

Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse Imaging.

Assessing fertilized human embryos is crucial for in vitro-fertilization (IVF), a task being revolutionized by artificial intelligence and deep learning. Existing models used for embryo quality assessment and chromosomal abnormality (ploidy) detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we developed and compared various embryo ploidy status prediction models across distinct embryo development stages. We present BELA (Blastocyst Evaluation Learning Algorithm), a state-of-the-art ploidy prediction model surpassing previous image- and video-based models, without necessitating subjective input from embryologists. BELA uses multitask learning to predict quality scores that are used downstream to predict ploidy status. By achieving an AUC of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy (PGT-A), BELA exemplifies how such models can streamline the embryo evaluation process, reducing time and effort required by embryologists.

Successful cryptozoospermia management with multiple semen specimen collection.

OBJECTIVE: To determine the prevalence of sperm suitable for intracytoplasmic sperm injection (ICSI) in fresh ejaculated semen samples provided by men scheduled for a microdissection testicular sperm extraction (mTESE) procedure. Secondary objectives included an evaluation of the effect of a short abstinence period on semen quality and ICSI outcomes for men with cryptozoospermia. DESIGN: Retrospective cohort study. SETTING: Academic medical center. PATIENTS: All men were scheduled to undergo a mTESE procedure by a single, high-volume surgeon at an academic center from September 1, 2015, to May 1, 2021. INTERVENTION: Presence of sperm suitable for ICSI in the ejaculate on the day of scheduled mTESE. MAIN OUTCOME MEASURES: Prevalence of sperm suitable for ICSI in the ejaculate among previously diagnosed men with azoospermia. Secondary outcomes included changes in semen parameters, clinical pregnancy rate, and live birth rate. RESULTS: Of 727 planned mTESE procedures, 69 (9.5%) were canceled because sperm suitable for ICSI were identified in a fresh ejaculated sample produced on the day of scheduled surgery (typically one day before oocyte retrieval). Overall, 50 men (50/727, 6.9%) used these rare, ejaculated sperm for ICSI. Semen samples obtained with <24 hours of abstinence were more likely to have better motility than the sample initially provided on the day of the planned mTESE. The live birth rate per ICSI attempt using these rare, ejaculated sperm was 36% (19/53). CONCLUSION: Providing a fresh ejaculated semen sample on the day of mTESE allows nearly 10% of men with azoospermia to avoid surgery with satisfactory ICSI outcomes. Providing multiple ejaculated samples over a short period of time does not adversely affect sperm concentration and may enhance sperm motility in men with cryptozoospermia.

Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection.

Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplasmic sperm injection (ICSI) have been explored, mainly to improve fertilization outcomes and decrease variability within ICSI procedures. Although significant advances have been achieved in developing algorithms that track and rank single sperm in real-time during ICSI, the clinical benefits these might have in improving pregnancy rates from a single assisted reproductive technology cycle remain to be established.

Human theca arises from ovarian stroma and is comprised of three discrete subtypes.

Theca cells serve multiple essential functions during the growth and maturation of ovarian follicles, providing structural, metabolic, and steroidogenic support. While the function of theca during folliculogenesis is well established, their cellular origins and the differentiation hierarchy that generates distinct theca sub-types, remain unknown. Here, we performed single cell multi-omics analysis of primary cell populations purified from human antral stage follicles (1-3 mm) to define the differentiation trajectory of theca/stroma cells. We then corroborated the temporal emergence and growth kinetics of defined theca/stroma subpopulations using human ovarian tissue samples and xenografts of cryopreserved/thawed ovarian cortex, respectively. We identified three lineage specific derivatives termed structural, androgenic, and perifollicular theca cells, as well as their putative lineage-negative progenitor. These findings provide a framework for understanding the differentiation process that occurs in each primordial follicle and identifies specific cellular/molecular phenotypes that may be relevant to either diagnosis or treatment of ovarian pathologies.

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.

BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. METHODS: In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC). FINDINGS: Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability. INTERPRETATION: As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A. FUNDING: US National Institutes of Health.

Xenograft model of heterotopic transplantation of human ovarian cortical tissue and its clinical relevance.

In brief: Xenografts of human ovarian cortical tissue provide a tractable model of heterotopic autotransplantation that is used for fertility preservation in patients undergoing ablative chemo/radiotherapy. This study describes the behavior of hundreds of xenografts to establish a framework for the clinical function of ovarian cortex following autotransplantation over short- and long-term intervals. Abstract: More than 200 live births have been achieved using autotransplantation of cryopreserved ovarian cortical fragments, yet challenges remain to be addressed. Ischemia of grafted tissue undermines viability and longevity, typically requiring transplantation of multiple cortical pieces; and the dynamics of recruitment within a graft and the influence of parameters like size and patient age at the time of cryopreservation are not well-defined. Here, we describe results from a series of experiments in which we xenografted frozen/thawed human ovarian tissue (n = 440) from 28 girls and women (age range 32 weeks gestational age to 46 years, median 24.3 ± 4.6). Xenografts were recovered across a broad range of intervals (1-52 weeks post-transplantation) and examined histologically to quantify follicle density and distribution. The number of antral follicles in xenografted cortical fragments correlated positively with the total follicle number and was significantly reduced with increased patient age. Within xenografts, follicles were distributed in focal clusters, similar to the native ovary, but the presence of a leading antral follicle coincided with increased proliferation of surrounding follicles. These results underscore the importance of transplanting ovarian tissue with a high density of follicles and elucidate a potential paracrine influence of leading antral follicles on neighboring follicles of earlier stages. This temporal framework for interpreting the kinetics of follicle growth/mobilization may be useful in setting expectations and guiding the parameters of clinical autotransplantation.

Error-prone repair of stalled replication forks drives mutagenesis and loss of heterozygosity in haploinsufficient BRCA1 cells.

Germline mutations in the BRCA genes are associated with a higher risk of carcinogenesis, which is linked to an increased mutation rate and loss of the second unaffected BRCA allele (loss of heterozygosity, LOH). However, the mechanisms triggering mutagenesis are not clearly understood. The BRCA genes contain high numbers of repetitive DNA sequences. We detected replication forks stalling, DNA breaks, and deletions at these sites in haploinsufficient BRCA cells, thus identifying the BRCA genes as fragile sites. Next, we found that stalled forks are repaired by error-prone pathways, such as microhomology-mediated break-induced replication (MMBIR) in haploinsufficient BRCA1 breast epithelial cells. We detected MMBIR mutations in BRCA1 tumor cells and noticed deletions-insertions (>50 bp) at the BRCA1 genes in BRCA1 patients. Altogether, these results suggest that under stress, error-prone repair of stalled forks is upregulated and induces mutations, including complex genomic rearrangements at the BRCA genes (LOH), in haploinsufficient BRCA1 cells.

Effects of Chemo- and Radiation Therapy on Microsurgical Testicular Sperm Extraction for Men with Nonobstructive Azoospermia.

PURPOSE: Men who survive cancer as children or young adults may have severe spermatogenic impairment with azoospermia requiring surgical sperm retrieval and assisted reproductive technologies. We assessed treatment outcomes from a large series of cancer patients with prior radiation and/or chemotherapy. MATERIALS AND METHODS: Men with nonobstructive azoospermia who underwent initial microsurgical testicular sperm extraction from 1995-2020 from a high-volume surgeon at a single institution were identified. Those with a history of malignancy treated by radiation therapy and/or chemotherapy were included. The primary outcome was successful sperm retrieval. RESULTS: A total of 106 men were evaluated, of whom 57 received chemotherapy and radiation, 44 received only chemotherapy and 5 received only radiation. Sperm retrieval was successful in 39 of 106 (37%) men, with higher likelihood of retrieval in men who received only chemotherapy compared to men who received chemotherapy and radiation (61% vs 18%, p <0.001). None of the 18 patients who received chemotherapy with radiation to the pelvis had successful sperm retrieval, compared to 26% of patients who received chemotherapy with extra-pelvic radiation (p=0.02). CONCLUSIONS: Chemotherapy and radiation for cancer may result in nonobstructive azoospermia that can be treated to allow fertility. However, pelvic radiation therapy is associated with the worst prognosis for successful treatment with microsurgical sperm retrieval and in vitro fertilization; we observed no cases of successful retrieval in men who received pelvic radiation therapy. These data are useful for pretreatment counseling, suggesting that men with prior radiation therapy may not be candidates for surgical sperm retrieval.

Chronic superphysiologic AMH promotes premature luteinization of antral follicles in human ovarian xenografts.

Anti-Müllerian hormone (AMH) is produced by growing ovarian follicles and provides a diagnostic measure of reproductive reserve in women; however, the impact of AMH on folliculogenesis is poorly understood. We cotransplanted human ovarian cortex with control or AMH-expressing endothelial cells in immunocompromised mice and recovered antral follicles for purification and downstream single-cell RNA sequencing of granulosa and theca/stroma cell fractions. A total of 38 antral follicles were observed (19 control and 19 AMH) at long-term intervals (>10 weeks). In the context of exogenous AMH, follicles exhibited a decreased ratio of primordial to growing follicles and antral follicles of increased diameter. Transcriptomic analysis and immunolabeling revealed a marked increase in factors typically noted at more advanced stages of follicle maturation, with granulosa and theca/stroma cells also displaying molecular hallmarks of luteinization. These results suggest that superphysiologic AMH alone may contribute to ovulatory dysfunction by accelerating maturation and/or luteinization of antral-stage follicles.

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.

OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use. DESIGN: Retrospective study. SETTING: Consortium of 11 assisted reproductive technology centers in the United States. PATIENT(S): Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Prediction of clinical pregnancy (fetal heartbeat). RESULT(S): The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful. CONCLUSION(S): This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.

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.

Testicular sperm characteristics in men with nonobstructive azoospermia and their impact on intracytoplasmic sperm injection outcome.

OBJECTIVE: To report fertilization and clinical pregnancy rates based on sperm characteristics at the time of intracytoplasmic sperm injection (ICSI) in men with nonobstructive azoospermia (NOA) following microdissection testicular sperm extraction (mTESE). DESIGN: Retrospective cohort. SETTING: Tertiary hospital. PATIENT(S): Men with NOA undergoing mTESE between 2013 and 2016 who had successful sperm retrieval and subsequent spermatozoa available for ICSI. INTERVENTION(S): Sperm characteristic assessment. MAIN OUTCOME MEASURE(S): Fertilization and clinical pregnancy rates. RESULT(S): One hundred ninety-eight men with NOA and successful mTESE were included. The mean ages of the patients and their partners were 35 ± 8 and 31 ± 5 years, respectively. The overall fertilization rate was 44%, and the clinical pregnancy rate was 38%. The absence of twitching sperm motility and the presence of an acrosome defect were associated with decreased fertilization and clinical pregnancy rates on univariable analysis. On multivariable analysis, the presence of motility was associated with higher fertilization rates and greater odds of clinical pregnancy (odds ratio, 4.37; 95% confidence interval, 1.61-11.85). An abnormal acrosome was associated with reduced odds of pregnancy (odds ratio, 0.40; 95% confidence interval, 0.18-0.85). No specific anomaly or combination of sperm abnormalities precluded fertilization or clinical pregnancy with ICSI. CONCLUSION(S): To our knowledge, this is the first comprehensive study evaluating the importance of sperm characteristics and their impact on ICSI outcomes in men with NOA. The results suggest that no specific defect, including the use of nonmotile testicular spermatozoa, precluded a chance of clinical pregnancy. The study evaluated sperm characteristics at the time of ICSI injection; initial evaluation at the time of retrieval may differ significantly from that of spermatozoa selected for ICSI.

Exogenous insulin-like growth factor 1 accelerates growth and maturation of follicles in human cortical xenografts and increases ovarian output in mice.

OBJECTIVE: To measure the influence of exogenous insulin-like growth factor 1 (IGF1) on follicle growth and maturation in human ovarian cortical xenografts. DESIGN: Xenotransplantation model. SETTING: University-based research laboratory. PATIENTS/ANIMALS: Ovarian tissue was donated with consent and institutional review board approval by brain-dead organ donors or patients undergoing ovarian tissue cryopreservation for fertility preservation. Cortical fragments were transplanted into immunocompromised mice. INTERVENTIONS: Cryopreserved ovarian cortical fragments from four women (aged 19, 25, 33, and 46 years) were transplanted into the gluteus muscle of immunocompromised mice in a fibrin matrix containing endothelial cells that were transduced with lentiviral particles encoding secreted IGF1. Xenografts were recovered after 3, 8, and 14 weeks. In addition, C57/Bl6 mice underwent intraovarian injection of saline or recombinant IGF1 (60 µg), followed by superovulation, analysis of ethynyl-deoxyuridine incorporation, and ribonucleic acid sequencing of the whole ovaries. MAIN OUTCOME MEASURES: For xenografts: follicle count and distribution; antral follicle count; and corpora lutea/albicans count. For mice: follicle count and distribution; oocyte yield, ethynyl-deoxyuridine incorporation (granulosa cell proliferation); and ovarian transcriptomic signature. RESULTS: At 3 weeks, xenografts in the IGF1 condition revealed a decreased percentage of primary follicles and increased percentage of secondary follicles that were concentrated in the preantral subtype; at 8 weeks, an increase in secondary follicles was concentrated in the simple subtype; after 14 weeks, primordial follicles were reduced, and while the number of advanced follicles did not power the experiment to demonstrate significance, antral follicles reduced and corpora lutea increased. Supporting experiments in mice revealed an increase in normal oocytes following intraovarian injection of recombinant IGF1 (60 µg) as well as increased proliferative index among follicles of secondary and preantral stages. Ribonucleic acid sequencing analysis of the whole ovaries following injection of recombinant IGF1 (25 µg) revealed an acute (24 hours) upregulation of transcripts related to steroidogenesis and luteinization. CONCLUSIONS: Exogenous IGF1 advances the pace of growth among primordial, primary, and secondary stage follicles but results in near absence of antral stage follicles in long-term (14 weeks) xenografts. In mice, acute administration of IGF1 promotes follicle advance and increased oocyte yield. The results suggest that while superphysiological IGF1 alone advances the pace of growth among early/preantral follicles, a sustained and/or later-stage influence undermines antral follicle growth/survival or promotes premature luteinization. These findings provide a temporal framework for interpreting follicle growth/mobilization and may be useful in understanding the clinical application of human growth hormone in the context of assisted reproduction.

Artificial intelligence in human in vitro fertilization and embryology.

Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.

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?

Comparison of Human Antral Follicles of Xenograft versus Ovarian Origin Reveals Disparate Molecular Signatures.

The activation, growth, and maturation of oocytes to an ovulatory phase, termed folliculogenesis, is governed by the orchestrated activity of multiple specialized cell types within the ovary; yet, the mechanisms governing diversification and behavior of discrete cellular sub-populations within follicles are poorly understood. We use bulk and single-cell RNA sequencing to distinguish the transcriptional signature of prospectively isolated granulosa and theca/stroma cell subsets within human antral follicles derived from xenografts or ovaries. The analysis deconstructs phenotypic diversification within small (<4 mm) antral follicles, identifying secreted factors that are differentially enriched between mural and oophorus granulosa cells, and segregating stromal/support and steroidal activity between theca externa and interna, respectively. Multiple factors are differentially expressed in follicles of xenograft versus ovarian origin. These data capture a high-resolution transcriptional signature of granulosa and theca subpopulations and provide a systems-level portrait of cellular diversification in early antral human follicles.