Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective
| Autor: | Kelly Tilleman, Ilse DeCroo, R.R. Wildeboer, C. Blank, Benedictus C. Schoot, Massimo Mischi, Petra De Sutter, Basiel Weyers |
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| Přispěvatelé: | Biomedical Diagnostics Lab, Signal Processing Systems, Center for Care & Cure Technology Eindhoven |
| Rok vydání: | 2019 |
| Předmět: |
Male
0301 basic medicine medicine.medical_specialty Sperm Injections medicine.medical_treatment Single Embryo Transfer Fertilization in Vitro Risk Assessment Decision Support Techniques Machine Learning 03 medical and health sciences Single Embryo Transfer/adverse effects 0302 clinical medicine Risk Factors Pregnancy medicine Humans Sperm Injections Intracytoplasmic Embryo Implantation Fertilization in Vitro/adverse effects Retrospective Studies 030219 obstetrics & reproductive medicine In vitro fertilisation Receiver operating characteristic Obstetrics business.industry Blastocyst Transfer Reproducibility of Results Obstetrics and Gynecology Retrospective cohort study Blastocyst transfer Infertility/diagnosis medicine.disease Intracytoplasmic prediction model Treatment Outcome Fertility 030104 developmental biology Reproductive Medicine IVF Infertility Cohort Female business Live birth random forest |
| Zdroj: | Fertility and Sterility, 111(2), 318-326. Agon Elsevier |
| ISSN: | 0015-0282 |
| DOI: | 10.1016/j.fertnstert.2018.10.030 |
| Popis: | Objective: To develop a random forest model (RFM) to predict implantation potential of a transferred embryo and compare it with a multivariate logistic regression model (MvLRM), based on data from a large cohort including in vitro fertilization (IVF) patients treated with the use of single-embryo transfer (SET) of blastocyst-stage embryos. Design: Retrospective study of a 2-year single-center cohort of women undergoing IVF or intracytoplasmatic sperm injection (ICSI). Setting: Academic hospital. Patient(s): Data from 1,052 women who underwent fresh SET in IVF or ICSI cycles were included. Intervention(s): None. Main Outcome Measure(s): The performance of both RFM and MvLRM to predict pregnancy was quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), classification accuracy, specificity, and sensitivity. Result(s): ROC analysis resulted in an AUC of 0.74 ± 0.03 for the proposed RFM and 0.66 ± 0.05 for the MvLRM for the prediction of ongoing pregnancies of ≥11 weeks. This RFM approach and the MvLRM yielded, respectively, sensitivities of 0.84 ± 0.07 and 0.66 ± 0.08 and specificities of 0.48 ± 0.07 and 0.58 ± 0.08. Conclusion(s): The performance to predict ongoing implantation will significantly improve with the use of an RFM approach compared with MvLRM. |
| Databáze: | OpenAIRE |
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