Embryo selection through artificial intelligence versus embryologists: a systematic review

STUDY QUESTION What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? SUMMARY ANSWER AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. WHAT IS KNOWN ALREADY The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. STUDY DESIGN, SIZE, DURATION The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: (‘Artificial intelligence’ OR ‘Machine Learning’ OR ‘Deep learning’ OR ‘Neural network’) AND (‘IVF’ OR ‘in vitro fertili*’ OR ‘assisted reproductive techn*’ OR ‘embryo’), where the character ‘*’ refers the search engine to include any auto completion of the search term. PARTICIPANTS/MATERIALS, SETTING, METHODS A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. MAIN RESULTS AND THE ROLE OF CHANCE Twenty articles were included in this review. There was no specific embryo assessment day across the studies—Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist’s visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59–94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists’ assessment following local respective guidelines. Using blind test datasets, the embryologists’ accuracy prediction was 65.4% (range 47–75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68–90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58–76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67–98%), while clinical embryologists had a median accuracy of 51% (range 43–59%). LIMITATIONS, REASONS FOR CAUTION The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. WIDER IMPLICATIONS OF THE FINDINGS AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers’ perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. STUDY FUNDING/COMPETING INTERESTS This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. REGISTRATION NUMBER CRD4202125633

Aspirin for evidence-based preeclampsia prevention trial: effect of aspirin in prevention of preterm preeclampsia in subgroups of women according to their characteristics and medical and obstetrical history.

Objective: To examine whether there are differences in the effect of aspirin on the incidence of preterm-PE in the ASPRE trial in subgroups defined according to maternal characteristics and medical and obstetrical history. Study design: This was a secondary analysis of data from the ASPRE trial. In ASPRE women with singleton pregnancies had screening by means of an algorithm that combines maternal factors and biomarkers at 11-13 weeks’ gestation. Those with an estimated risk for preterm-PE of >1 in 100 were invited to participate in a double-blind trial of aspirin (150 mg/day) vs. placebo from 11 to 14 until 36 weeks’ gestation. Aspirin was associated with a significant reduction in the incidence of preterm-PE with delivery at 90% of the prescribed medication. Results are presented as forest plot with P values for the interaction effects, group sizes, event counts and estimated odds ratios. We examined whether the test of interaction was significant at the 5% level with a Bonferroni adjustment for multiple comparisons. Results: There was no evidence of heterogeneity in the aspirin effect in subgroups defined according to maternal characteristics and obstetrical history. In participants with chronic hypertension preterm-PE occurred in 10.2% (5/49) in the aspirin group and in 8.2% (5/61) in the placebo group (adjusted odds ratio 1.29, 95% confidence interval, 0.33 to 5.12); the respective values in those without chronic hypertension were 1.1% (8/749) in the aspirin group and 3.9% (30/761) in the placebo group (adjusted odds ratio 0.27, 95% confidence interval, 0.12 to 0.60). In all participants with adherence of >90% the adjusted odds ratio in the aspirin group was 0.24 (95% CI 0.09 to 0.65), in the subgroup with chronic hypertension it was 2.06 (95% CI 0.40 to 10.71) and in those without chronic hypertension it was 0.05 (95% CI 0.01 to 0.41). For the complete data set the test of interaction was not significant at the 5% level (p=0.055), but in those with adherence >90%, after adjustment for multiple comparisons, the interaction was significant at the 5% level (p=0.0019). Conclusions: The beneficial effect of aspirin in the prevention of preterm preeclampsia may not apply in pregnancies with chronic hypertension. There was no evidence of heterogeneity in the aspirin effect in subgroups defined according to maternal characteristics and obstetrical history

Prediction of preterm birth with and without preeclampsia using mid-pregnancy immune and growth-related molecular factors and maternal characteristics.

OBJECTIVE:To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia. STUDY DESIGN:Included were 400 women with singleton deliveries in California in 2009-2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15-20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS:Twenty-five serum biomarkers along with maternal age <34 years and poverty status identified >80% of women with PTB ± preeclampsia with best performance in women with preterm preeclampsia (AUC = 0.889, 95% confidence interval (0.822-0.959) training; 0.883 (0.804-0.963) testing). CONCLUSION:Together with maternal age and poverty status, mid-pregnancy immune and growth factors reliably identified most women who went on to have a PTB ± preeclampsia

Screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation

Objective: To examine the performance of screening for early-, preterm- and term-preeclampsia (PE) at 11 13 weeks’ gestation by maternal factors and combinations of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum placental growth factor (PLGF) and serum pregnancy associated plasma protein A (PAPP A). Methods The data for this study were derived from three previously reported prospective non intervention screening studies at 11+0 – 13+6 weeks’ gestation in a combined total of 61,174 singleton pregnancies, including 1,770 (2.9%) that developed PE. Bayes theorem was used to combine the prior distribution of the gestational age at delivery with PE, obtained from maternal characteristics, with various combinations of biomarker multiple of the median (MoM) values to derive the p patient specific risks of delivery with PE at <37 weeks’ gestation. The performance of such screening was estimated. Results In pregnancies that develop ed PE , compared to those without PE, the MoM values of UtA-PI and MAP were increased and PAPP A and PLGF were decreased and the deviation from normal was greater for early than late PE for all four biomarkers. Combined screening by maternal factors, UtA-PI, MAP and PLGF predicted 90% of early PE, 75% of preterm PE and 4 1 % of term PE, at screen positive rate of 10%; inclusion of PAPP A did not improve the performance of screening The performance of screening depended on the racial origin of the women; in screening by a combination of maternal factors, MAP, UtA-PI and PLGF and use of the risk cut off of 1 in 10 0 for PE at <37 weeks in Caucasian women, the screen positive rate was 10% and detection rates for early --, preterm and term PE were 88%, 69% and 40%, respectively. With the same method of screening and risk cut off in women of Afro Caribbean racial origin, the screen positive rate was 34% and detection rates for early --, preterm and term PE were 100%, 92% and 75%, respectively. Conclusion Screening by maternal factors and biomarkers at 11-13 weeks’ gestation can identify a high proportion of pregnancies that develop early- and preterm-PE

First Trimester Examination of Fetal Anatomy: Clinical Practice Guideline by the World Association of Perinatal Medicine (WAPM) and the Perinatal Medicine Foundation (PMF)

This recommendation document follows the mission of the World Association of Perinatal Medicine in collaboration with the Perinatal Medicine Foundation. We aim to bring together groups and individuals throughout the world for precise standardization to implement the ultrasound evaluation of the fetus in the first trimester of pregnancy and improve the early detection of anomalies and the clinical management of the pregnancy. The aim is to present a document that includes statements and recommendations on the standard evaluation of the fetal anatomy in the first trimester, based on quality evidence in the peer-reviewed literature as well as the experience of perinatal experts around the world.info:eu-repo/semantics/publishedVersio

Can Dietary Patterns Impact Fertility Outcomes? A Systematic Review and Meta-Analysis.

There are conflicting results on the effect of diet on fertility. This study aimed to assess the effect of different dietary patterns on fertility outcomes in populations who conceive spontaneously and those requiring assisted reproductive technology (ART). A systematic search and meta-analysis were performed for studies investigating dietary patterns or whole diets in reproductive aged women requiring ART or conceived naturally. Outcomes were live births, pregnancy rates and infertility rates. In amount of 15,396 studies were screened with 11 eligible studies. Ten different diet patterns were grouped broadly into categories: Mediterranean, Healthy or Unhealthy. For the Mediterranean diet, on excluding high risk-of-bias studies (n = 3), higher adherence was associated with improved live birth/pregnancy rates in ART [OR 1.91 (95% CI 1.14-3.19, I2 43%)] (n = 2). Adherence to various Healthy diets was associated with improved ART outcomes (ProFertility diet and Dutch Dietary Guidelines) and natural conception outcomes (Fertility diet). However, due to the variability in Healthy diets' components, results were not pooled. Studies demonstrated preliminary evidence for the role of dietary patterns or whole diets in improving pregnancy and live birth rates. However, due to heterogeneity across the literature it is currently unclear which diet patterns are associated with improvements in fertility and ART outcomes.Hugo G. Winter, Daniel L. Rolnik, Ben W. J. Mol, Sophia Torkel, Simon Alesi, Aya Mousa, Nahal Habibi, Thais R. Silva, Tin Oi Cheung, Chau Thien Tay, Alejandra Quinteros, Jessica A. Grieger, and Lisa J. Mora