A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> INTRODUCTION <s> Objective To assess whether the use of clinical prediction models improves concordance between gynaecologists with respect to treatment decisions in reproductive medicine. ::: ::: ::: ::: Design We constructed 16 vignettes of subfertile couples by varying fertility history, postcoital test, sperm motility, follicle-stimulating hormone level and Chlamydia antibody titre. ::: ::: ::: ::: Setting Thirty-five gynaecologists estimated three probabilities, i.e. the 1-year probability of spontaneous pregnancy, the pregnancy chance after intrauterine insemination (IUI) and the pregnancy chance after in vitro fertilisation (IVF). Subsequently they proposed therapeutic regimens for these 16 fictional couples, i.e. expectant management, IUI or IVF. Three months later, the participant gynaecologists again had to propose therapeutic regimes for the same 16 fictional cases but this time accompanied by pregnancy chances obtained from prediction models: predictions on spontaneous pregnancy, IUI and IVF. ::: ::: ::: ::: Population Thirty-five gynaecologists working in academic and nonacademic hospitals in the Netherlands. ::: ::: ::: ::: Methods Setting section. ::: ::: ::: ::: Main outcome measures The concordance between gynaecologists of probability estimates, expressed as interclass correlation coefficient (ICC) and the concordance between gynaecologists of treatment decisions, analysed by calculating Cohen’s kappa (κ). ::: ::: ::: ::: Results The gynaecologists differed widely in estimating pregnancy chances (ICC: 0.34). Furthermore, there was a huge variation in the proposed therapeutic regimens (κ: 0.21). The treatment decisions made by gynaecologists were consistent with the ranking of their probability estimates. When prediction models were used, the concordance (κ) for treatment decisions increased from 0.21 to 0.38. The number of gynaecologists counselling for expectant management increased from 39 to 51%, whereas counselling for IVF dropped from 23 to 14%. ::: ::: ::: ::: Conclusion Gynaecologists differed widely in their estimation of prognosis in 16 fictional cases of subfertile couples. Their therapeutic regimens showed likewise huge variation. After confrontation with prediction models in the same 16 fictional cases, the proposed therapeutic regimens showed only slightly better concordance. Therefore a simple introduction of validated prediction models is insufficient to introduce concordant management between doctors. <s> BIB001 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> INTRODUCTION <s> BACKGROUND ::: Many definitions used in medically assisted reproduction (MAR) vary in different settings, making it difficult to standardize and compare procedures in different countries and regions. With the expansion of infertility interventions worldwide, including lower resource settings, the importance and value of a common nomenclature is critical. The objective is to develop an internationally accepted and continually updated set of definitions, which would be utilized to standardize and harmonize international data collection, and to assist in monitoring the availability, efficacy, and safety of assisted reproductive technology (ART) being practiced worldwide. ::: ::: ::: METHOD ::: Seventy-two clinicians, basic scientists, epidemiologists and social scientists gathered together at the WHO headquarters in Geneva, Switzerland in December, 2008. Several months in advance, three working groups were established which were responsible for terminology in three specific areas: clinical conditions and procedures, laboratory procedures and outcome measures. Each group reviewed the existing ICMART glossary, made recommendations for revisions and introduced new terms to be considered for glossary expansion. ::: ::: ::: RESULTS ::: A consensus was reached on 87 terms, expanding the original glossary by 34 terms, which included definitions for numerous clinical and laboratory procedures. Special emphasis was placed in describing outcome measures such as cumulative delivery rates and other markers of safety and efficacy in ART. ::: ::: ::: CONCLUSIONS ::: Standardized terminology should assist in analysis of worldwide trends in MAR interventions and in the comparison of ART outcomes across countries and regions. This glossary will contribute to a more standardized communication among professionals responsible for ART practice, as well as those responsible for national, regional and international registries. <s> BIB002 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> INTRODUCTION <s> The IVF ET method is a scientifically recognized infertility treatment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treatment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process. <s> BIB003 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> INTRODUCTION <s> background: Infertility is estimated to affect as many as 186 million people worldwide. Although male infertility contributes to more than half of all cases of global childlessness, infertility remains a woman’s social burden. Unfortunately, areas of the world with the highest rates of infertility are often thosewith poor access to assisted reproductive techniques (ARTs). In such settings, womenmay be abandoned to their childless destinies. However, emerging data suggest that making ART accessible and affordable is an important gender intervention. To that end, this article presents an overview of what we know about global infertility, ART and changing gender relations, posing five key questions: (i) why is infertility an ongoing global reproductive health problem? (ii)What are the gender effects of infertility, and are they changing over time? (iii)What do we know about the globalization of ART to resource-poor settings? (iv) How are new global initiatives attempting to improve access to IVF? (v) Finally, what can be done to overcome infertility, help the infertile and enhance low-cost IVF (LCIVF) activism? methods: An exhaustive literature review using MEDLINE, Google Scholar and the keyword search function provided through the Yale University Library (i.e. which scans multiple databases simultaneously) identified 103 peer-reviewed journal articles and 37 monographs, chapters and reports from the years 2000–2014 in the areas of: (i) infertility demography, (ii) ART in low-resource settings, (iii) gender and infertility in low-resource settings and (iv) the rise of LCIVF initiatives. International Federation of Fertility Societies Surveillance reports were particularly helpful in identifying important global trends in IVF clinic distribution between 2002 and 2010. Additionally, a series of articles published by scholars who are tracking global cross-border reproductive care (CBRC) trends, as well as others who are involved in the growing LCIVF movement, were invaluable. results: Recent global demographic surveys indicate that infertility remains anongoing reproductive problem,with six key demographic features. Despite themassive global expansion of ART services over the past decade (2005–2015), ART remains inaccessible inmany parts of theworld, particularly in sub-Saharan Africa, where IVF clinics are still absent in most countries. For women living in such ART-poor settings, the gender effects of infertilitymay bedevastating. In contrast, inART-rich regions such as theMiddle East, thenegative gender effectsof infertility arediminishing over time, especiallywith state subsidizationofART. Furthermore,menare increasingly acknowledging theirmale infertility and seeking ICSI.Thus, access toART <s> BIB004 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> INTRODUCTION <s> In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment. <s> BIB005 </s> A Review of Machine Learning Approaches in Assisted Reproductive Technologies <s> INTRODUCTION <s> OBJECTIVE ::: To develop a prediction model to estimate the chances of a live birth over multiple complete cycles of in vitro fertilisation (IVF) based on a couple's specific characteristics and treatment information. ::: ::: ::: DESIGN ::: Population based cohort study. ::: ::: ::: SETTING ::: All licensed IVF clinics in the UK. National data from the Human Fertilisation and Embryology Authority register. ::: ::: ::: PARTICIPANTS ::: All 253 417 women who started IVF (including intracytoplasmic sperm injection) treatment in the UK from 1999 to 2008 using their own eggs and partner's sperm. ::: ::: ::: MAIN OUTCOME MEASURE ::: Two clinical prediction models were developed to estimate the individualised cumulative chance of a first live birth over a maximum of six complete cycles of IVF-one model using information available before starting treatment and the other based on additional information collected during the first IVF attempt. A complete cycle is defined as all fresh and frozen-thawed embryo transfers arising from one episode of ovarian stimulation. ::: ::: ::: RESULTS ::: After exclusions, 113 873 women with 184 269 complete cycles were included, of whom 33 154 (29.1%) had a live birth after their first complete cycle and 48 925 (43.0%) after six complete cycles. Key pretreatment predictors of live birth were the woman's age (31 v 37 years; adjusted odds ratio 1.66, 95% confidence interval 1.62 to 1.71) and duration of infertility (3 v 6 years; 1.09, 1.08 to 1.10). Post-treatment predictors included number of eggs collected (13 v 5 eggs; 1.29, 1.27 to 1.32), cryopreservation of embryos (1.91, 1.86 to 1.96), the woman's age (1.53, 1.49 to 1.58), and stage of embryos transferred (eg, double blastocyst v double cleavage; 1.79, 1.67 to 1.91). Pretreatment, a 30 year old woman with two years of unexplained primary infertility has a 46% chance of having a live birth from the first complete cycle of IVF and a 79% chance over three complete cycles. If she then has five eggs collected in her first complete cycle followed by a single cleavage stage embryo transfer (with no embryos left for freezing) her chances change to 28% and 56%, respectively. ::: ::: ::: CONCLUSIONS ::: This study provides an individualised estimate of a couple's cumulative chances of having a baby over a complete package of IVF both before treatment and after the first fresh embryo transfer. This novel resource may help couples plan their treatment and prepare emotionally and financially for their IVF journey. <s> BIB006
Infertility is defined as failure to achieve pregnancy after 12 months of unprotected intercourse BIB002 . According to the World Health Organization (WHO), "infertility is a disease which generates disability as an impairment of function" (2) . Infertility affects more than 186 million couples in worldwide that majority of them are deprived of proper treatments in developing countries. So, infertility is known as a most common global health problem BIB004 . There are various types of approaches for conducting the infertility treatment, such as lifestyle changes, drug therapy, surgical methods and assisted reproductive technologies (ART)  . ARTs are advanced technologies but overall success rate of the ART's is less than 30% BIB006 . ART includes multiple stages that are complex, time-consuming (more than approximately two weeks), expensive and frustrating for infertile couples   . Opposite to general perception, ART does not grantee the success. Despite the multiple ART cycles, 38-49% of couples remain in-fertile  . Therefore, it is necessary that infertile couples should be well informed about their probability of success. Families should decide on ART as a treatment with considering the success chance, financial burden, physical and emotional risks, multiple pregnancies and complex process of treatment   . Reliable prediction of ART outcome is a significant challenge  . Clinicians are not able to correctly predict treatment outcome, and there is a weak concordance between them on treatment decisions and pregnancy probability estimation BIB001 . To overcome these problems and provide accurate patient-specific prediction of pregnancy chances, utilizing from computational prediction models are possible solutions. Clinical prediction models are analytical techniques that discover predictive impact of various related information, and consequently estimate the treatment results with significant confidence level. The prediction models fed with patient's personal parameters and other effective ART A Review of Machine Learning Approaches in Assisted Reproductive Technologies cycle specific variables  BIB003 BIB005 ).