{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:22:36Z","timestamp":1792599756292,"version":"4.5.0"},"reference-count":48,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:p>Assisted reproductive technology (ART) is one of the major developments that has had a significant impact on infertility treatment. A predictive model of ART success based on machine learning (ML) techniques can provide a robust basis for estimating treatment success. This study aimed to identify predictive models of ART success and their determinants. A systematic search was conducted in PubMed, Web of Science, Scopus, and Embase. Data extraction involved collecting data in studies on dataset characteristics, ML techniques, and predictive model performance indicators. The search resulted in 3655 records, of which 27 papers were selected for analysis. ML publications in ART prediction have been in the past 5 years. In general, 107 various features were reported in all reviewed studies. Female age was the most common feature used in all identified studies. Most studies (96.3%) applied a supervised approach to develop predictive models. Among all, support vector machine (SVM) was the most frequently applied technique (44.44%). Nineteen different indicators have been used in studies to evaluate the model performance. 74.07% of the reviewed papers reported area under the receiver operating characteristic (ROC) curve (AUC) as their performance indicator. Accuracy (55.55%), sensitivity (40.74%), and specificity (25.92%) were also commonly reported. ML has the potential to bring hope to infertile couples and to facilitate making challenging decisions. Considering relevant contributing factors and ML techniques is critical for reliable predictive modeling.<\/jats:p>","DOI":"10.4103\/jehp.jehp_1798_23","type":"journal-article","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:42:09Z","timestamp":1743345729000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":5,"title":["Machine learning in predicting infertility treatment success: A systematic literature review of techniques"],"prefix":"10.4103","volume":"14","author":[{"given":"Shirin","family":"Dehghan","sequence":"first","affiliation":[{"name":"Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Moghaddasi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Rabiei","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Choobineh","sequence":"additional","affiliation":[{"name":"Department of Laboratory Sciences, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keivan","family":"Maghooli","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Science and Research Branch Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mojtaba","family":"Vahidi-Asl","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,3]]},"reference":[{"key":"R1-20250328","doi-asserted-by":"crossref","first-page":"254","DOI":"10.34297\/AJBSR.2019.05.000922","article-title":"Infertility information system with an approach to data architecture: A systematic review","volume":"5","author":"Seyedi","year":"2019","journal-title":"Am J Biomed Sci Res"},{"key":"R3-20250328","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.clinbiochem.2018.03.012","article-title":"Fertility and infertility: Definition and epidemiology","volume":"62","author":"Vander","year":"2018","journal-title":"Clin Biochem"},{"key":"R5-20250328","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rbmo.2018.12.001","article-title":"Towards the global coverage of a unified registry of IVF outcomes","volume":"38","author":"Fauser","year":"2019","journal-title":"Reprod BioMed Online"},{"key":"R7-20250328","doi-asserted-by":"crossref","first-page":"451915","DOI":"10.5402\/2012\/451915","article-title":"Adverse outcomes of IVF\/ICSI pregnancies vary depending on aetiology of infertility","volume":"2012","author":"Kuivasaari-Pirinen","year":"2012","journal-title":"ISRN Obstet Gynecol"},{"key":"R8-20250328","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s12884-019-2213-y","article-title":"Complications and adverse outcomes in pregnancy and childbirth among women who conceived by assisted reproductive technologies: A nationwide birth cohort study of Japan environment and children\u2019s study","volume":"19","author":"Nagata","year":"2019","journal-title":"BMC Pregnancy Childbirth"},{"key":"R9-20250328","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.emc.2019.01.005","article-title":"Complications of assisted reproductive technology","volume":"37","author":"Hilbert","year":"2019","journal-title":"Emerg Med Clin North Am"},{"key":"R10-20250328","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1093\/humupd\/dmv016","article-title":"Infertility around the globe: New thinking on gender, reproductive technologies and global movements in the 21st century","volume":"21","author":"Inhorn","year":"2015","journal-title":"Hum Reprod Update"},{"key":"R11-20250328","first-page":"e120300","article-title":"Impact of computerized provider order entry on chemotherapy medication errors: A systematic review","volume":"15","author":"Afrash","year":"2022"},{"key":"R12-20250328","doi-asserted-by":"crossref","first-page":"278","DOI":"10.4103\/jehp.jehp_14_22","article-title":"A qualitative approach to identify clinical uncertainty in practicing physicians and clinical residents","volume":"11","author":"Bigdeli","year":"2022","journal-title":"J Educ Health Promot"},{"key":"R13-20250328","doi-asserted-by":"crossref","first-page":"e273","DOI":"10.1016\/j.fertnstert.2019.07.808","article-title":"The application of machine learning methods to evaluate predictors of live birth in programmed thaw cycles","volume":"112","author":"Vaughan","year":"2019","journal-title":"Fertil Steril"},{"key":"R14-20250328","doi-asserted-by":"crossref","first-page":"2030010","DOI":"10.1142\/S021812662030010X","article-title":"Machine learning techniques for assisted reproductive technology: A review","volume":"29","author":"Ranjini","year":"2020","journal-title":"J Circuits Syst Comput"},{"key":"R15-20250328","doi-asserted-by":"crossref","first-page":"297","DOI":"10.31661\/jbpe.v0i0.2109-1403","article-title":"Prediction of breast cancer using machine learning approaches","volume":"12","author":"Rabiei","year":"2022","journal-title":"J Biomed Phys Eng"},{"key":"R16-20250328","doi-asserted-by":"crossref","first-page":"R139","DOI":"10.1530\/REP-18-0523","article-title":"Artificial intelligence in reproductive medicine","volume":"158","author":"Wang","year":"2019","journal-title":"Reproduction"},{"key":"R17-20250328","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s13643-021-01626-4","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"10","author":"Page","year":"2021","journal-title":"Syst Rev"},{"key":"R18-20250328","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Ann Intern Med"},{"key":"R19-20250328","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12958-023-01065-x","article-title":"A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data","volume":"21","author":"Tian","year":"2023","journal-title":"Reprod Biol Endocrinol"},{"key":"R20-20250328","doi-asserted-by":"crossref","first-page":"e0267554","DOI":"10.1371\/journal.pone.0267554","article-title":"Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization","volume":"17","author":"Wang","year":"2022","journal-title":"PLoS One"},{"key":"R21-20250328","doi-asserted-by":"crossref","first-page":"7216","DOI":"10.1038\/s41598-022-10902-9","article-title":"Internal validation and comparison of predictive models to determine success rate of infertility treatments: A retrospective study of 2485 cycles","volume":"12","author":"Mehrjerd","year":"2022","journal-title":"Sci Rep"},{"key":"R22-20250328","doi-asserted-by":"crossref","first-page":"105780","DOI":"10.1016\/j.cmpb.2020.105780","article-title":"Predicting and improving the probability of live birth for women undergoing frozen-thawed embryo transfer: A data-driven estimation and simulation model","volume":"198","author":"Liang","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"R23-20250328","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1007\/s10815-021-02224-y","article-title":"Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?","volume":"38","author":"Kozar","year":"2021","journal-title":"J Assist Reprod Genet"},{"key":"R24-20250328","first-page":"128","article-title":"Factors associated with in vitro fertilization live birth outcome: A comparison of different classification methods","volume":"15","author":"Amini","year":"2021","journal-title":"Int J Fertil Steril"},{"key":"R25-20250328","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1177\/1460458219892138","article-title":"Computational prediction of implantation outcome after embryo transfer","volume":"26","author":"Raef","year":"2020","journal-title":"Health Inform J"},{"key":"R26-20250328","doi-asserted-by":"crossref","first-page":"20925","DOI":"10.1038\/s41598-020-76928-z","article-title":"Machine learning predicts live-birth occurrence before in-vitro fertilization treatment","volume":"10","author":"Goyal","year":"2020","journal-title":"Sci Rep"},{"key":"R27-20250328","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10815-019-01498-7","article-title":"An artificial neural network for the prediction of assisted reproduction outcome","volume":"36","author":"Vogiatzi","year":"2019","journal-title":"J Assist Reprod Genet"},{"key":"R28-20250328","first-page":"17317","article-title":"Personalized prediction of live birth prior to the first in vitro fertilization treatment: A machine learning method","author":"Qiu","year":"2019","journal-title":"J Transl Med"},{"key":"R29-20250328","doi-asserted-by":"crossref","first-page":"679","DOI":"10.31661\/JBPE.V0I0.1187","article-title":"Use of artificial neural networks and pca to predict results of infertility treatment in the icsi method","volume":"9","author":"Mostaar","year":"2019","journal-title":"J Biomed Phys Eng"},{"key":"R30-20250328","doi-asserted-by":"crossref","first-page":"130460","DOI":"10.1109\/ACCESS.2019.2940588","article-title":"In vitro fertilization (IVF) cumulative pregnancy rate prediction from basic patient characteristics","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"R31-20250328","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fertnstert.2018.10.030","article-title":"Prediction of implantation after blastocyst transfer in in vitro fertilization: A machine-learning perspective","volume":"111","author":"Blank","year":"2019","journal-title":"Fertil Steril"},{"key":"R32-20250328","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1007\/s00521-018-3693-9","article-title":"A machine learning approach for prediction of pregnancy outcome following IVF treatment","volume":"32","author":"Hassan","year":"2018","journal-title":"Neural Comput Appl"},{"key":"R33-20250328","first-page":"184","article-title":"Predicting implantation outcome of in vitro fertilization and intracytoplasmic sperm injection using data mining techniques","volume":"11","author":"Hafiz","year":"2017","journal-title":"Int J Fertil Steril"},{"key":"R34-20250328","first-page":"507","article-title":"Predicting IVF outcome: A proposed web-based system using artificial intelligence","volume":"30","author":"Siristatidis","year":"2016","journal-title":"In vivo (Athens, Greece)"},{"key":"R35-20250328","first-page":"7","article-title":"Prediction of infertility treatment outcomes using classification trees. Stud. Log. Gramm","volume":"47","author":"Milewska","year":"2016","journal-title":"Rhetor"},{"key":"R36-20250328","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1515\/slgr-2015-0038","article-title":"Significance of discriminant analysis in prediction of pregnancy in IVF treatment","volume":"43","author":"Milewska","year":"2015","journal-title":"Stud Log Gramm Rhetor"},{"key":"R37-20250328","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s11517-015-1299-2","article-title":"Estimating the chance of success in IVF treatment using a ranking algorithm","volume":"53","author":"G\u00fcvenir","year":"2015","journal-title":"Med Biol Eng Comput"},{"key":"R38-20250328","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1177\/0272989X14535984","article-title":"Predictive modeling of implantation outcome in an in vitro fertilization setting: An application of machine learning methods","volume":"35","author":"Uyar","year":"2015","journal-title":"Med Decis Making"},{"key":"R39-20250328","first-page":"126","article-title":"An integrated methodology of artificial neural network and rough set theory for analyzing IVF data","author":"Durairaj","year":"2014","journal-title":"2014 International Conference on Intelligent Computing Applications, Coimbatore"},{"key":"R41-20250328","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1093\/humrep\/det030","article-title":"Objective way to support embryo transfer: A probabilistic decision","volume":"28","author":"Gianaroli","year":"2013","journal-title":"Hum Reprod"},{"key":"R42-20250328","doi-asserted-by":"crossref","first-page":"4437","DOI":"10.1016\/j.eswa.2010.09.112","article-title":"Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes","volume":"38","author":"Guh","year":"2011","journal-title":"Expert Syst Appl"},{"key":"R46-20250328","doi-asserted-by":"crossref","first-page":"e35465","DOI":"10.2196\/35465","article-title":"Contributions of artificial intelligence reported in obstetrics and gynecology journals: Systematic review","volume":"24","author":"Dhombres","year":"2022","journal-title":"J Med Internet Res"},{"key":"R47-20250328","doi-asserted-by":"crossref","first-page":"112","DOI":"10.5958\/0976-5506.2015.00084.4","article-title":"Analysis of factors causing infertility in women using statistical analysis and association rule mining","volume":"6","author":"Meena","year":"2015","journal-title":"Indian J Public Health Res Dev"},{"key":"R48-20250328","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s10815-019-01408-x","article-title":"Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018","volume":"36","author":"Curchoe","year":"2019","journal-title":"J Assist Reprod Genet"},{"key":"R49-20250328","doi-asserted-by":"crossref","first-page":"334","DOI":"10.4103\/jehp.jehp_298_23","article-title":"Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines","volume":"12","author":"Narasimhan","year":"2023","journal-title":"J Educ Health Promot"},{"key":"R50-20250328","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.1007\/s10815-020-01950-z","article-title":"AI in the treatment of fertility: Key considerations","volume":"37","author":"Swain","year":"2020","journal-title":"J Assist Reprod Genet"},{"key":"R51-20250328","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1007\/s10815-020-01881-9","article-title":"Artificial intelligence in the IVF laboratory: Overview through the application of different types of algorithms for the classification of reproductive data","volume":"37","author":"Fernandez","year":"2020","journal-title":"J Assist Reprod Genet"},{"key":"R53-20250328","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.medj.2021.04.006","article-title":"Machine learning in clinical decision making","volume":"2","author":"Adlung","year":"2021","journal-title":"Med"},{"key":"R54-20250328","doi-asserted-by":"crossref","first-page":"140","DOI":"10.3390\/a13060140","article-title":"Special issue on ensemble learning and applications","volume":"13","author":"Pintelas","year":"2020","journal-title":"Algorithms"},{"key":"R55-20250328","doi-asserted-by":"crossref","first-page":"307","DOI":"10.3389\/fpubh.2017.00307","article-title":"Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice","volume":"5","author":"Trevethan","year":"2017","journal-title":"Front Public Health"},{"key":"R56-20250328","doi-asserted-by":"crossref","first-page":"e66844","DOI":"10.1371\/journal.pone.0066844","article-title":"Systematic review of the empirical evidence of study publication bias and outcome reporting bias \u2014 An updated review","volume":"8","author":"Dwan","year":"2013","journal-title":"PLoS One"}],"container-title":["Journal of Education and Health Promotion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jehp.jehp_1798_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T19:41:52Z","timestamp":1786650112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jehp.jehp_1798_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.4103\/jehp.jehp_1798_23","relation":{},"ISSN":["2277-9531","2319-6440"],"issn-type":[{"value":"2277-9531","type":"print"},{"value":"2319-6440","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"2023-11-04","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-22","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}