Estimating the chance of success in IVF treatment using a ranking algorithm

Güvenir, H. Altay; Misirli, Gizem; Dilbaz, Serdar; Özdeğirmenci, Özlem; Demır, Berfu; Dilbaz, Berna

doi:10.1007/s11517-015-1299-2

Cited by 44 publications

(30 citation statements)

References 30 publications

(34 reference statements)

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“…The area under the receiver-operating characteristic (ROC) curve is accepted as a reliable and popular performance measure for assessing the quality of classification algorithms in machine learning approaches. 18 The high value of AUC in this study (Table 3) shows the reliability of the presented approach for ET prediction.…”

Section: Discussionsupporting

confidence: 53%

“…In support of this result, in three studies, 6,40,60 among the five studies using the RF algorithm along with others, the RF was identified as a better method. Also, in previous studies, 18,39 the RF algorithm has achieved performance close to that of the superior algorithm. More studies in this domain have applied approaches based on classical statistical methods, such as logistic regression.…”

Section: Discussionmentioning

confidence: 71%

“…Clinical prediction models estimate the treatment results with a strong likelihood and also allow the treatment process to be adapted by using a variety of related parameters such as patient parameters and other effective ART cycle-specific variables. 8,17,18 To develop more accurate prediction models and algorithms with high-performance capacity, advanced computational approaches and data mining methods could be employed. 19 The focus of machine learning and data mining techniques is on developing computerized modeling and efficient predictive algorithms by detecting the hidden patterns in data to discover knowledge with high predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionsupporting

confidence: 53%

Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

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Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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“…Patient data were recorded with a computer-based program designed for developing a new technique called Success Estimation Using a Ranking Algorithm (SERA), using the RIMARC (ranking instances by maximizing the area under the ROC curve) algorithm [6]. The study group included 459 consecutive patients with DOR who met the inclusion criteria and were scheduled for their first ICSI-ET cycles at the Reproductive Endocrinology and Infertility Department of Etlik Zübeyde Hanım Women's Health Training and Research Hospital between December 2010 and July 2017.…”

Section: Methodsmentioning

confidence: 99%

The impact of different etiologies of diminished ovarian reserve on pregnancy outcome in IVF-ET cycles

Özelçi¹,

Aldemir²,

Dilbaz³

et al. 2019

Turk J Med Sci

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Background/aim: Diminished ovarian reserve (DOR) represents a major challenge in reproductive medicine, as it is often associated with poor ovarian stimulation response, high cycle cancellation rate, and low pregnancy rate. The aim of the present study is to compare the clinical pregnancy rates in intracytoplasmic sperm injection-embryo transfer (ICSI-ET) cycles in patients with different DOR etiologies. Materials and methods:Patient data were recorded with a computer-based program called Success Estimation Using a Ranking Algorithm (SERA). Overall, 459 patients were divided into 3 groups according to their DOR etiologies (Group A: idiopathic, n = 81; Group B: age-related, n = 294; Group C: previous ovarian surgery, n = 84). Results:Out of 459 stimulation cycles, 378 (82.4%) reached the oocyte retrieval stage, while 201 (43.8%) had embryo transfers. There was no significant difference between the patients with different DOR etiologies in terms of embryo transfer and cycle cancellation rate. The patients who had embryo transfer were 44 (52.4%) in Group A, 38 (46.9%) in Group B, and 119 (40.5%) in Group C. There were no significant differences between the three groups (P = 0.114). The percentages of women who had oocyte retrieval were 84.5% in Group A, 70% in Group B, and 80.3% in Group C (P = 0.104). While clinical pregnancy per transfer was 35.8% in Group A, 19.8% in Group B, and 29.5% in Group C, there was no statistically significant difference between the groups (P = 0.113). Conclusion:Although ovulation induction and ICSI-ET outcomes, including clinical pregnancy and live birth rates, were not significantly different with regards to the etiology of DOR, young women with DOR may benefit from assisted reproductive techniques.

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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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Estimating the chance of success in IVF treatment using a ranking algorithm

Cited by 44 publications

References 30 publications

Computational prediction of implantation outcome after embryo transfer

Computational prediction of implantation outcome after embryo transfer

The impact of different etiologies of diminished ovarian reserve on pregnancy outcome in IVF-ET cycles

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

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