Pharmacologic Characterization of LTGO-33, a Selective Small Molecule Inhibitor of the Voltage-Gated Sodium Channel NaV1.8 with a Unique Mechanism of Action.

Discovery and development of new molecules directed against validated pain targets is required to advance the treatment of pain disorders. Voltage-gated sodium channels (NaVs) are responsible for action potential initiation and transmission of pain signals. NaV1.8 is specifically expressed in peripheral nociceptors and has been genetically and pharmacologically validated as a human pain target. Selective inhibition of NaV1.8 can ameliorate pain while minimizing effects on other NaV isoforms essential for cardiac, respiratory, and central nervous system physiology. Here we present the pharmacology, interaction site, and mechanism of action of LTGO-33, a novel NaV1.8 small molecule inhibitor. LTGO-33 inhibited NaV1.8 in the nM potency range and exhibited over 600-fold selectivity against human NaV1.1-NaV1.7 and NaV1.9. Unlike prior reported NaV1.8 inhibitors that preferentially interacted with an inactivated state via the pore region, LTGO-33 was state-independent with similar potencies against closed and inactivated channels. LTGO-33 displayed species specificity for primate NaV1.8 over dog and rodent NaV1.8 and inhibited action potential firing in human dorsal root ganglia neurons. Using chimeras combined with mutagenesis, the extracellular cleft of the second voltage-sensing domain was identified as the key site required for channel inhibition. Biophysical mechanism of action studies demonstrated that LTGO-33 inhibition was relieved by membrane depolarization, suggesting the molecule stabilized the deactivated state to prevent channel opening. LTGO-33 equally inhibited wild-type and multiple NaV1.8 variants associated with human pain disorders. These collective results illustrate LTGO-33 inhibition via both a novel interaction site and mechanism of action previously undescribed in NaV1.8 small molecule pharmacologic space. SIGNIFICANCE STATEMENT: NaV1.8 sodium channels primarily expressed in peripheral pain-sensing neurons represent a validated target for the development of novel analgesics. Here we present the selective small molecule NaV1.8 inhibitor LTGO-33 that interdicts a distinct site in a voltage-sensor domain to inhibit channel opening. These results inform the development of new analgesics for pain disorders.

Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey.

PURPOSE OF REVIEW: This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolutionizing patient outcomes and operational efficiency by addressing challenges in fertility diagnoses and procedures. RECENT FINDINGS: Recent advancements in artificial intelligence, including machine learning and predictive modeling, are making significant strides in optimizing IVF processes such as medication dosing, scheduling, and embryological assessments. Innovations include artificial intelligence augmented diagnostic testing, predictive modeling for treatment outcomes, scheduling optimization, dosing and protocol selection, follicular and hormone monitoring, trigger timing, and improved embryo selection. These developments promise to refine treatment approaches, enhance patient engagement, and increase the accuracy and scalability of fertility treatments. SUMMARY: The integration of artificial intelligence into reproductive medicine offers profound implications for clinical practice and research. By facilitating personalized treatment plans, standardizing procedures, and improving the efficiency of fertility clinics, artificial intelligence technologies pave the way for value-based, accessible, and efficient fertility services. Despite the promise, the full potential of artificial intelligence in ART will require ongoing validation and ethical considerations to ensure equitable and effective implementation.

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.

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

PURPOSE: To evaluate the association, if any, between the grade of the trophectoderm (TE) and the rate at which ß-human-chorionic gonadotropin (ß-HCG) rises in early pregnancy. METHODS: This is a retrospective cohort study including 1116 singleton clinical pregnancies resulting from in vitro fertilization with single day 5 blastocyst transfer at an academic fertility center. TE quality was assessed by trained embryologists employing standard criteria. Three groups were formed based on the TE grade: grade A (n = 358), grade B (n = 628), and grade C (n = 130). Main outcome measure was the rise (%) in serum levels of ß-HCG (days 12 to 14 post embryo transfer), using the following formula [(ß-HCG D14 - ß-HCG D12) * 100/ß-HCG D12]. RESULTS: Fresh embryo transfers accounted for 64.1% of the population. Overall, in adjusted models there were no significant differences in the ß-HCG% rise when comparing the TE grade C group to TE grade A [adjß (95%CI): 10.09 (- 0.05, 20.22)] or when comparing TE grade Β group to TE grade A [4.46 (- 2.97, 11.88)]. When the analysis was restricted to fresh embryo transfers, significant differences were observed in the % rise of ß-HCG when comparing the TE grade C group to TE grade A [adjß (95%CI): 21.71 (5.67, 37.74)], but not when comparing the TE grade B group to TE grade A [2.68 (- 5.59, 10.95)]. In frozen transfers, there were no significant differences. CONCLUSION: TE grade appears to impact early pregnancy serum ß-HCG levels in the setting of a fresh day 5 embryo transfer, even after adjusting for potential confounders.

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.

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.

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.

Thyroid hormone levels associate with exposure to polychlorinated biphenyls and polybrominated biphenyls in adults exposed as children.

BACKGROUND: Michigan residents were directly exposed to endocrine-disrupting compounds, polybrominated biphenyl (PBB) and polychlorinated biphenyl (PCB). A growing body of evidence suggests that exposure to certain endocrine-disrupting compounds may affect thyroid function, especially in people exposed as children, but there are conflicting observations. In this study, we extend previous work by examining age of exposure's effect on the relationship between PBB exposure and thyroid function in a large group of individuals exposed to PBB. METHODS: Linear regression models were used to test the association between serum measures of thyroid function (total thyroxine (T4), total triiodothyronine (T3), free T4, free T3, thyroid stimulating hormone (TSH), and free T3: free T4 ratio) and serum PBB and PCB levels in a cross-sectional analysis of 715 participants in the Michigan PBB Registry. RESULTS: Higher PBB levels were associated with many thyroid hormones measures, including higher free T3 (p = 0.002), lower free T4 (p = 0.01), and higher free T3: free T4 ratio (p = 0.0001). Higher PCB levels were associated with higher free T4 (p = 0.0002), and higher free T3: free T4 ratio (p = 0.002). Importantly, the association between PBB and thyroid hormones was dependent on age at exposure. Among people exposed before age 16 (N = 446), higher PBB exposure was associated with higher total T3 (p = 0.01) and free T3 (p = 0.0003), lower free T4 (p = 0.04), and higher free T3: free T4 ratio (p = 0.0001). No significant associations were found among participants who were exposed after age 16. No significant associations were found between TSH and PBB or PCB in any of the analyses conducted. CONCLUSIONS: This suggests that both PBB and PCB are associated with thyroid function, particularly among those who were exposed as children or prenatally.

Ethylene Contributes to maize insect resistance1-Mediated Maize Defense against the Phloem Sap-Sucking Corn Leaf Aphid.

Signaling networks among multiple phytohormones fine-tune plant defense responses to insect herbivore attack. Previously, it was reported that the synergistic combination of ethylene (ET) and jasmonic acid (JA) was required for accumulation of the maize insect resistance1 (mir1) gene product, a cysteine (Cys) proteinase that is a key defensive protein against chewing insect pests in maize (Zea mays). However, this study suggests that mir1-mediated resistance to corn leaf aphid (CLA; Rhopalosiphum maidis), a phloem sap-sucking insect pest, is independent of JA but regulated by the ET-signaling pathway. Feeding by CLA triggers the rapid accumulation of mir1 transcripts in the resistant maize genotype, Mp708. Furthermore, Mp708 provided elevated levels of antibiosis (limits aphid population)- and antixenosis (deters aphid settling)-mediated resistance to CLA compared with B73 and Tx601 maize susceptible inbred lines. Synthetic diet aphid feeding trial bioassays with recombinant Mir1-Cys Protease demonstrates that Mir1-Cys Protease provides direct toxicity to CLA. Furthermore, foliar feeding by CLA rapidly sends defensive signal(s) to the roots that trigger belowground accumulation of the mir1, signifying a potential role of long-distance signaling in maize defense against the phloem-feeding insects. Collectively, our data indicate that ET-regulated mir1 transcript accumulation, uncoupled from JA, contributed to heightened resistance to CLA in maize. In addition, our results underscore the significance of ET acting as a central node in regulating mir1 expression to different feeding guilds of insect herbivores.

Investigation of an Outbreak of Variant Influenza A(H3N2) Virus Infection Associated With an Agricultural Fair-Ohio, August 2012.

BACKGROUND: In 2012, one third of cases in a multistate outbreak of variant influenza A(H3N2) virus ([H3N2]v) infection occurred in Ohio. We conducted an investigation of (H3N2)v cases associated with agricultural Fair A in Ohio. METHODS: We surveyed Fair A swine exhibitors and their household members. Confirmed cases had influenza-like illness (ILI) and a positive laboratory test for (H3N2)v, and probable cases had ILI. We calculated attack rates. We determined risk factors for infection, using multivariable log-binomial regression. RESULTS: We identified 20 confirmed and 94 probable cases associated with Fair A. Among 114 cases, the median age was 10 years, there were no hospitalizations or deaths, and 82% had swine exposure. In the exhibitor household cohort of 359 persons (83 households), we identified 6 confirmed cases (2%) and 40 probable cases (11%). An age of <10 years was a significant risk factor (P < .01) for illness. One instance of likely human-to-human transmission was identified. CONCLUSIONS: In this (H3N2)v outbreak, no evidence of sustained human-to-human (H3N2)v transmission was found. Our risk factor analysis contributed to the development of the recommendation that people at increased risk of influenza-associated complications, including children aged <5 years, avoid swine barns at fairs during the 2012 fair season.

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

Objective: To determine whether endometrial thickness (EMT) differs between i) clomiphene citrate (CC) and gonadotropin (Gn) utilizing patients as their own controls, and ii) patients who conceived with CC and those who did not. Furthermore, to investigate the association between late-follicular EMT and pregnancy outcomes, in CC and Gn cycles. Methods: Retrospective study. Three sets of analyses were conducted separately for the purpose of this study. In analysis 1, we included all cycles from women who initially underwent CC/IUI (CC1, n=1252), followed by Gn/IUI (Gn1, n=1307), to compare EMT differences between CC/IUI and Gn/IUI, utilizing women as their own controls. In analysis 2, we included all CC/IUI cycles (CC2, n=686) from women who eventually conceived with CC during the same study period, to evaluate EMT differences between patients who conceived with CC (CC2) and those who did not (CC1). In analysis 3, pregnancy outcomes among different EMT quartiles were evaluated in CC/IUI and Gn/IUI cycles, separately, to investigate the potential association between EMT and pregnancy outcomes. Results: In analysis 1, when CC1 was compared to Gn1 cycles, EMT was noted to be significantly thinner [Median (IQR): 6.8 (5.5-8.0) vs. 8.3 (7.0-10.0) mm, p<0.001]. Within-patient, CC1 compared to Gn1 EMT was on average 1.7mm thinner. Generalized linear mixed models, adjusted for confounders, revealed similar results (coefficient: 1.69, 95% CI: 1.52-1.85, CC1 as ref.). In analysis 2, CC1 was compared to CC2 EMT, the former being thinner both before [Median (IQR): 6.8 (5.5-8.0) vs. 7.2 (6.0-8.9) mm, p<0.001] and after adjustment (coefficient: 0.59, 95%CI: 0.34-0.85, CC1 as ref.). In analysis 3, clinical pregnancy rates (CPRs) and ongoing pregnancy rates (OPRs) improved as EMT quartiles increased (Q1 to Q4) among CC cycles (p<0.001, p<0.001, respectively), while no such trend was observed among Gn cycles (p=0.94, p=0.68, respectively). Generalized estimating equations models, adjusted for confounders, suggested that EMT was positively associated with CPR and OPR in CC cycles, but not in Gn cycles. Conclusions: Within-patient, CC generally resulted in thinner EMT compared to Gn. Thinner endometrium was associated with decreased OPR in CC cycles, while no such association was detected in Gn cycles.

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusion: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.

Outbreak of variant influenza A(H3N2) virus in the United States.

BACKGROUND: Variant influenza virus infections are rare but may have pandemic potential if person-to-person transmission is efficient. We describe the epidemiology of a multistate outbreak of an influenza A(H3N2) variant virus (H3N2v) first identified in 2011. METHODS: We identified laboratory-confirmed cases of H3N2v and used a standard case report form to characterize illness and exposures. We considered illness to result from person-to-person H3N2v transmission if swine contact was not identified within 4 days prior to illness onset. RESULTS: From 9 July to 7 September 2012, we identified 306 cases of H3N2v in 10 states. The median age of all patients was 7 years. Commonly reported signs and symptoms included fever (98%), cough (85%), and fatigue (83%). Sixteen patients (5.2%) were hospitalized, and 1 fatal case was identified. The majority of those infected reported agricultural fair attendance (93%) and/or contact with swine (95%) prior to illness. We identified 15 cases of possible person-to-person transmission of H3N2v. Viruses recovered from patients were 93%-100% identical and similar to viruses recovered from previous cases of H3N2v. All H3N2v viruses examined were susceptible to oseltamivir and zanamivir and resistant to adamantane antiviral medications. CONCLUSIONS: In a large outbreak of variant influenza, the majority of infected persons reported exposures, suggesting that swine contact at an agricultural fair was a risk for H3N2v infection. We identified limited person-to-person H3N2v virus transmission, but found no evidence of efficient or sustained person-to-person transmission. Fair managers and attendees should be aware of the risk of swine-to-human transmission of influenza viruses in these settings.

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.

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 Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies.

Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.

Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes.

Because of the birth of the first baby after in vitro fertilization (IVF), the field of assisted reproductive technologies (ARTs) has seen significant advancements in the past 40 years. Over the last decade, the healthcare industry has increasingly adopted machine learning algorithms to improve patient care and operational efficiency. Artificial intelligence (AI) in ovarian stimulation is a burgeoning niche that is currently benefiting from increased research and investment from both the scientific and technology communities, leading to cutting-edge advancements with promise for rapid clinical integration. AI-assisted IVF is a rapidly growing area of research that can improve ovarian stimulation outcomes and efficiency by optimizing the dosage and timing of medications, streamlining the IVF process, and ultimately leading to increased standardization and better clinical outcomes. This review article aims to shed light on the latest breakthroughs in this area, discuss the role of validation and potential limitations of the technology, and examine the potential of these technologies to transform the field of assisted reproductive technologies. Integrating AI responsibly into IVF stimulation will result in higher-value clinical care with the goal of having a meaningful impact on enhancing access to more successful and efficient fertility treatments.

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.

Predicting polycystic ovary syndrome (PCOS) with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusions: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.