An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.
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ABSTRACT:
SUBMITTER: Huang B
PROVIDER: S-EPMC8667440 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.
| S-EPMC8809568 | biostudies-literature
Project description:PurposeThe rate of embryonic aneuploidy increases with increasing female age and is the primary cause of lower pregnancy and live birth rates (LBR) in older reproductive age women. This retrospective cohort study evaluates single euploid embryo transfers to determine whether an age-related decline in reproductive efficiency persists.MethodsA total of 8175 non-donor single embryo transfers (SET) after pre-implantation testing for aneuploidy (PGT-A) and cryopreservation were included. These were divided into five groups by patient age: < 35 years old (n = 3789 embryos transferred), 35-37 (n = 2200), 38-40 (n = 1624), 41-42 (n = 319), and > 42 (n = 243). Implantation rate (IR), clinical pregnancy rate (CPR), and LBR were calculated for each group as a percentage of embryos transferred and compared. CPR was also analyzed as a percentage of implanted pregnancies, and LBR as a percentage of clinical pregnancies, to determine when age has the greatest impact. These results were then adjusted for confounding variables via a multivariate logistic regression model.ResultsImplantation rates negatively correlated with age. After adjusting for confounders, women 38 years or older had a significantly lower IR than those under 35 (OR 0.85, 95%CI 0.73-0.99 for 38-40 years old; 0.69, 0.53-0.91 for 41-42, and 0.69, 0.51-0.94 for > 42). These differences are also apparent in CPR and LBR. The rates of progression to clinical pregnancy and live birth did not differ significantly by age group. Other factors observed to affect IR independently were anti-Müllerian hormone (AMH), day of embryo transfer, and embryo morphology.ConclusionWhile selection of euploid embryos may be effective in overcoming a significant proportion of the age-related decline in reproductive efficiency, a decrease in IR, CPR, and LBR persists even when analyzing only euploid embryo transfers. The observed impact of aging is, therefore, independent of ploidy, as well as of other variables that affect reproductive efficiency. These results indicate that factors other than aneuploidy contribute to reproductive senescence.
| S-EPMC7125286 | biostudies-literature
Project description:Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model. In a sequential prediction setting, COMPOSER achieved a consistently high area under the curve (AUC) (ICU: 0.925-0.953; ED: 0.938-0.945). Out of over 6 million prediction windows roughly 20% and 8% were identified as indeterminate amongst non-septic and septic patients, respectively. COMPOSER provided early warning within a clinically actionable timeframe (ICU: 12.2 [3.2 22.8] and ED: 2.1 [0.8 4.5] hours prior to first antibiotics order) across all six cohorts, thus allowing for identification and prioritization of patients at high risk for sepsis.
| S-EPMC8429719 | biostudies-literature
Project description:PurposeThis paper aims to investigate the efficacy of IVF with preimplantation genetic testing for aneuploidy (PGT-A), using only best-scoring blastocysts from young (≤ 35 years) infertile patients undergoing single blastocyst frozen embryo transfers (FET).MethodIn this randomized controlled trial (RCT) registered 29 March 2017, 302 infertile patient-couples eligible to participate underwent autologous ICSI blastocyst freeze-all cycles. Two-hundred and twenty patient-couples satisfied the inclusion criteria (i.e., female age ≤ 35 years, two-day 5 ≥ 2BB blastocysts) and were randomized to either the PGT-A (PGT-A group, n = 109) selection arm or morphology score (morphology group, n = 111) selection arm. In both arms, the highest ranking (by morphological score) blastocysts were selected for FET.ResultsOf the 109 best-scoring blastocysts that underwent PGT-A, 80 were predicted to be euploid (73.4%) and were transferred in FET (euploid subgroup). There was no statistical difference in LB rate between the euploid subgroup and morphology group (56.3% vs 58.6%, odds ratio 0.91 (95% CI 0.51-1.63), p = 0.750). In a multiple logistic regression, the transfer of euploid blastocysts was not found to be a significant predictor of LB when adjusting for female age, infertility duration, antral follicle count, and blastocyst quality, with the independent odds expressed as 0.91 (95% CI 0.50-1.66, p = 0.760).ConclusionIn young (≤ 35 years) infertile patients with at least two ≥ 2BB blastocysts, PGT-A blastocyst selection does not result in an enhanced LB rate, with the evidence suggesting that the effectivity of PGT-A may be limited by the effectivity of TE biopsy.Trial registrationClinicalTrials.gov ID: NCT03095053.
| S-EPMC6505013 | biostudies-literature
Project description:BACKGROUND:In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. METHODS:We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. RESULTS:The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). CONCLUSIONS:The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.
| S-EPMC7057604 | biostudies-literature
Project description:As a byproduct of increasing infertility cases, the use of medically assisted reproduction (MAR) has increased. As such, the need to gain information regarding the implantation potential of specific MAR preimplantation embryos prior to transfer has become increasingly critical. One potential source of this information is contained in the blastocoel fluid from day 5/6 embryos. This fluid contains cell-free DNA, proteins, RNA, metabolites, exosomes, etc., and analysis of these contents provides clinicians with an opportunity to gain more data regarding potential of each embryo. While application of preimplantation genetic testing for aneuploidies (PGT-A) may be limited to women of advanced maternal age or with recurrent pregnancy loss, the fluid taken at the time of embryo biopsy can be analyzed for any frozen embryo as well as PGT-A embryos. In both cases, blastocoel fluid analysis provides information regarding a preimplantation embryo's potential for implantation. Moreover, as remnants of apoptosis, embryonic cell-free DNA (cfDNA) and mRNA may lead clinicians to better understand and predict the extent of self-correction occurring within the preimplantation embryo. While analysis of blastocoel components are not yet viable replacements for PGT-A, their study may still reveal critical clinical information about the implantation potential for any given embryo.
| S-EPMC7028688 | biostudies-literature
Project description:PurposeTo evaluate whether endometrial compaction using sequential transvaginal ultrasound is associated with improved live birth rates in medicated single euploid frozen embryo transfer (FET) cycles.MethodsProspective observational cohort study at a private fertility clinic. Patients who underwent FETs between January and December 2018 were assessed for inclusion. The change in endometrial thickness between the end of the estrogen phase and the day before embryo transfer, measured by sequential transvaginal ultrasound, was used to categorize cycles with compaction (≥ 5%), no change, or expansion (≥ 5%). FET cycle outcomes were then compared between groups. The primary outcome was live birth. Secondary outcomes include clinical pregnancy rate and rate of spontaneous abortion.ResultsOf the 259 single euploid medicated FETs performed during the study period, only 43/259 (16.6%) of the cycles demonstrated ≥ 5% compaction, whereas 152/259 (58.7%) expanded and 64/259 (24.7%) were unchanged. Live birth rates did not differ between cycles with compaction (58.1%), no change (54.7%), or expansion (58.6%), p = 0.96. Clinical pregnancy and spontaneous abortion rates were also similar between groups.ConclusionThe vast majority of cycles did not demonstrate endometrial compaction. Endometrial compaction is not associated with live birth rate or spontaneous abortion rate in medicated single euploid FETs in this cohort.
| S-EPMC7884553 | biostudies-literature
Project description:Background In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24?h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14?days. Results We used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24?h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P?<?0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P?<?0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.
| S-EPMC7541213 | biostudies-literature
Project description:Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer-target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer-target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.
| S-EPMC8038094 | biostudies-literature
Project description:Abstract Background Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). Methods We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction–confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. Results A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m2 (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24–3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71–1.82). Conclusions High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.
| S-EPMC8244656 | biostudies-literature