The application of neural networks in predicting the outcome of in- vitro fertilization

Kaufmann, Sarah; Eastaugh, J.; Snowden, S.; Smye, S W; Sharma, Vinay

doi:10.1093/humrep/12.7.1454

Cited by 72 publications

(46 citation statements)

References 15 publications

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“…Among the implemented algorithms, NN and RF showed superior performance to NB, SVM, kNN, and DT models, particularly by using a reduced and ranked feature set and optimized threshold of sampling (10-fold cross-validation). The NN algorithm has been used in two studies 46,56 as single technique, and in other studies, it has been used along with other algorit hms. 6,25,37,39,[57][58][59][60] In accordance with our results, the NN algorithm in comparison with other algorithms has been selected as a more suitable method in ART outcome prediction.…”

Section: Discussionmentioning

confidence: 99%

Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

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Section: Discussionmentioning

confidence: 99%

Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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“…Research in ART is represented well due to the fact that pharmacological intervention is necessary for several common ART practices. AI methods have been applied to inform and advise physicians [14], to predict pregnancy success [15][16][17][18], to provide optimized treatment [19], and to understand miscarriage risk [20].…”

Section: Assisted Reproductive Technologiesmentioning

confidence: 99%

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Davidson

Boland

2020

J Pharmacokinet Pharmacodyn

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The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and SCOPUS. Search terms included pregnancy and AI. Research articles and book chapters were included, while conference papers, editorials and notes were excluded from the review. Included papers focused on pregnancy and AI methods, and pertained to pharmacologic interventions. We identified 376 distinct studies from our queries. A final set of 31 papers were included for the review. Included papers represented a variety of pregnancy concerns and multidisciplinary applications of AI. Few studies relate to pregnancy, AI, and pharmacologics and therefore, we review carefully those studies. External validation of models and techniques described in the studies is limited, impeding on generalizability of the studies. Our review describes how AI has been applied to address maternal health, throughout the pregnancy process: preconception, prenatal, perinatal, and postnatal health concerns. However, there is a lack of research applying AI methods to understand how pharmacologic treatments affect pregnancy. We identify three areas where AI methods could be used to improve our understanding of pharmacological effects of pregnancy, including: (a) obtaining sound and reliable data from clinical records (15 studies), (b) designing optimized animal experiments to validate specific hypotheses (1 study) to (c) implementing decision support systems that inform decision-making (11 studies). The largest literature gap that we identified is with regards to using AI methods to optimize translational studies between animals and humans for pregnancyrelated drug exposures.

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“…This is essential in order to predict outcomes which could lead to shorter time to pregnancy and result in a healthy, live-birth. In 1997, Kaufmann et al developed an ANN with a predictive power of 59% using only four inputs (age, number of eggs recovered, number of embryos transferred and whether there was embryo freezing) in order to predict the likelihood of successful IVF (21). Obstacles to greater sensitivity in this area include the many unknown factors which lead to a successful outcome for IVF which are needed to 'teach' for the purpose of ML (22).…”

Section: Ai In Ivfmentioning

confidence: 99%