Revolutionizing Reproduction: The Impact of Robotics and Artificial Intelligence (AI) in Assisted Reproductive Technology: A Comprehensive Review
DOI:10.7759/cureus.63072 by Cureus. in 2024
[1133]
TextBook
Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatis
DOI:10.1055/s-0044-1791536 by Seminars in Reproductive Medicine in 2024
[1301]
Artificial intelligence model utilizing endometrial analysis to contribute as a predictor of assisted reproductive technology success
DOI:10.46989/001c.115893 by Journal of IVF-Worldwide in 2024
[1130]
Global Andrology Forum. Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics
DOI:10.5534/wjmh.230050 by World Journal of Men's Health in 2024
[912]
Criteria for implementing artificial intelligence systems in reproductive medicine
DOI:10.5653/cerm.2023.06009 by Clinical and Experimental Reproductive Medicine in 2023
[676]
Artificial intelligence as a door opener for a new era of human reproduction
DOI:10.1093/hropen/hoad043 by Human Reproduction Open in 2023
[661]
Role of Artificial Intelligence in Quality Assurance in ART: A Review
DOI:10.1142/S2661318223300015 by Fertility & Reproduction in 2023
[369]
AI in the treatment of fertility: key considerations.
DOI:10.1007/s10815-020-01950-z by Journal of Assisted Reproduction and Genetics in 2020
[38]
Recent Publications
Innovations in reproductive medicine, Gartner hype cycle and Dunning–Kruger effect
DOI:10.1016/j.rbmo.2024.104702 by Reproductive BioMedicine Online in 2024
[2266]
Large language models to facilitate pregnancy prediction after in vitro fertilization
DOI:10.1111/aogs.14989 by Acta Obstetricia et Gynecologica Scandinavica in 2024
[2214]
A review of artificial intelligence applications in in vitro fertilization
DOI:10.1007/s10815-024-03284-6 by Journal of Assisted Reproduction and Genetics in 2024
[2138]
Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction
DOI:10.1371/journal.pone.0310829 by PLoS One. in 2024
[2126]
Artificial intelligence in reproductive endocrinology: an in-depth longitudinal analysis of ChatGPTv4's month-by-month interpretation and adherence to clinical guidelines for diminished ovarian reserve
DOI:10.1007/s12020-024-04031-8 by Endocrine in 2024
[2055]
EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool
DOI:10.1038/s42003-024-05960-w by Communications Biology [Nature] in 2024
[2046]
Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images
DOI:10.1038/s41598-024-52241-x by Scientific Reports in 2024
[2045]
Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?
DOI:10.3389/fendo.2024.1449035 by Frontiers in Endocrinology (Lausanne) in 2024
[1940]
Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights
DOI:10.1186/s12958-024-01285-9 by Reproductive Biology and Endocrinology in 2024
[1927]
Use and understanding of AI in the ART laboratory: an international survey
DOI:10.1016/j.rbmo.2024.104435 by Reproductive BioMedicine Online in 2024
[1870]
Machine learning tool to predict number of metaphase II oocytes and trigger day at the start of ovarian stimulation cycle: step towards personalized ovarian stimulation treatment
DOI:10.1016/j.rbmo.2024.104441 by Reproductive BioMedicine Online in 2024
[1868]
Development of an AI-based support system for controlled ovarian stimulation
DOI:10.1002/rmb2.12603 by Reproductive Medicine and Biology in 2024
[1849]
Looking into the future: a machine learning powered prediction model for oocyte return rates following cryopreservation
DOI:10.1016/j.rbmo.2024.104432 by Reproductive BioMedicine Online in 2024
[1818]
Nomogram for predicting live birth in ovulatory women undergoing frozen-thawed embryo transfer
DOI:10.1186/s12884-024-06759-7 by BMC Pregnancy and Childbirth in 2024
[1805]
Development of an IVF prediction model for donor oocytes: a retrospective analysis of 10 877 embryo transfers
DOI:10.1093/humrep/deae174 by Human Reproduction in 2024
[1760]
Testing an artificial intelligence algorithm to predict fetal heartbeat of vitrified-warmed blastocysts from a single image: predictive ability in different settings
DOI:10.1093/humrep/deae178 by Human Reproduction in 2024
[1759]
Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center, prospective study
DOI:0.1038/s41598-024-69165-1 by Scientific Reports in 2024
[1731]
A New Decision-Support Tool in a Multi-Center Randomized Trial for Personalized, Optimized, and Simplified Fertility Treatment in non-PCOS Patients
DOI:10.1530/RAF-24-0013 by Reproduction and Fertility in 2024
[1724]
Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification
DOI:10.1186/s12958-024-01271-1 by Reproductive Biology and Endocrinology in 2024
[1645]
Microfluidic chips in female reproduction: a systematic review of status, advances, and challenges
DOI:10.7150/thno.97301 by Theranostics in 2024
[1638]
Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization
DOI:10.1093/biomethods/bpae052 by Biology Methods & Protocols in 2024
[1636]
Artificial intelligence model utilizing endometrial analysis to contribute as a predictor of assisted reproductive technology success
DOI:10.46989/001c.115893 by Journal of IVF-Worldwide in 2024
[1612]
Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models
DOI:10.3389/fendo.2024.1378157 by Frontiers in Endocrinology (Lausanne) in 2024
[1411]
The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation
DOI:10.1186/s12958-024-01251-5 by Reproductive Biology and Endocrinology in 2024
[1372]
Clinical data-based modeling of IVF live birth outcome and its application
DOI:10.1186/s12958-024-01253-3 by Reproductive Biology and Endocrinology in 2024
[1351]
Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians
DOI:10.4103/aja202431 by Asian Journal of Andrology in 2024
[1338]
Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review
DOI:10.2196/53396 by Journal of Medical Internet Research in 2024
[1318]
Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?
DOI:10.1093/humrep/deae144 by Human Reproduction in 2024
[1312]
Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation
DOI:10.1007/s10815-024-03178-7 by Journal of Assisted Reproduction and Genetics in 2024
[1303]
A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study
DOI:10.1002/ijgo.15739 by International Journal of Gynecology & Obstetrics in 2024
[1219]
Development and evaluation of a usable blastocyst predictive model using the biomechanical properties of human oocytes
DOI:10.1371/journal.pone.0299602 by PLoS One. in 2024
[1179]
Webinars
Computer Shows Why: Visualizing Machine Learning and Decision [II3]
The International IVF Initiative - i3
BRILLIANT MINDS: NEW APPROACHES IN ART AND FERTILITY TREATMENT
The International IVF Initiative - i3
Artificial Intelligence in ART
The International IVF Initiative - i3
PRECISION MEDICINE IN REPRODUCTION: TIME FOR PREDICTION AND PREVENTION
The International IVF Initiative - i3
Embryo selection with artificial intelligence: how to evaluate and compare methods
VITROLIFE
What Genomics Dreams Might Come
The International IVF Initiative - i3
AI, UNDER THE HOOD
The International IVF Initiative - i3
Sub topics related to :AI, algorithms and IVF.
ID
Course
Artificial Intelligence, Deep Learning and IVF+
References related to SubTopic
Simopoulou M, Sfakianoudis K, Maziotis E, et al. Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence. J Assist Reprod Genet. 2018,35(9):1545-1557. doi:10.1007/s10815-018-1266-6 - ID:2696
Wang R, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019,158(4):R139-R154. doi:10.1530/REP-18-0523 - ID:2697
VerMilyea M, Hall JMM, Diakiw SM, et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum Reprod. 2020,35(4):770-784. doi:10.1093/humrep/deaa013 - ID:2698
Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019,34(6):1011-1018. doi:10.1093/humrep/dez064 - ID:2699
Uyar A, Bener A, Ciray HN. Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods. Med Decis Making. 2015,35(6):714-725. doi:10.1177/0272989X14535984 - ID:2700
Zaninovic N, Elemento O, Rosenwaks Z. Artificial intelligence: its applications in reproductive medicine and the assisted reproductive technologies. Fertil Steril. 2019,112(1):28-30. doi:10.1016/j.fertnstert.2019.05.019 - ID:2701
Ratna MB, Bhattacharya S, Abdulrahim B, McLernon DJ. A systematic review of the quality of clinical prediction models in in vitro fertilisation. Hum Reprod. 2020,35(1):100-116. doi:10.1093/humrep/dez258 - ID:2702
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019,110:12-22. doi:10.1016/j.jclinepi.2019.02.004 - ID:2703
Raef B, Ferdousi R. A Review of Machine Learning Approaches in Assisted Reproductive Technologies. Acta Inform Med. 2019,27(3):205-211. doi:10.5455/aim.2019.27.205-211 - ID:2704
Letterie G. Mac Donald A. Artificial intelligence in IVF: a computer decision support system for day to day management of ovarian stimulation during in vitro fertilization. Fertil Steril. 2020, 114 (XXX–XX) https://doi.org/10.1016/j.fertnstert.2019.07.20 - ID:2705
Chavez-Badiola A, Flores-Saiffe-Farías A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod Biomed Online. 2020 Jul 5:S1472-6483(20)30373-4. doi: 10.1016/j.rbmo.2020.07.003 - ID:2802
Curchoe CL. All Models Are Wrong, but Some Are Useful. J Assist Reprod Genet. 2020 Oct 7. doi: 10.1007/s10815-020-01895-3 - ID:2820
Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. 2019 Apr,36(4):591-600. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504989/ - ID:2821
Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Kandula H, Hariton E, Souter I, Dimitriadis I, Ramirez LB, Curchoe CL, Swain J, Boehnlein LM, Shafiee H. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020 Sep 15,9:e55301. doi: 10.7554/eLife.55301 - ID:2822
Fernandez EI, Ferreira AS, Cecílio MHM, Chéles DS, de Souza RCM, Nogueira MFG, Rocha JC. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J Assist Reprod Genet. 2020 Jul 11. doi: 10.1007/s10815-020-01881-9 - ID:2823
Bori L, Dominguez F, Fernandez EI, Del Gallego R, Alegre L, Hickman C, Quiñonero A, Nogueira MFG, Rocha JC, Meseguer M. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Reprod Biomed Online. 2020 Oct 8:S1472-6483(20)30537-X. doi: 10.1016/j.rbmo.2020.09.031 - ID:2948
Bori L, Paya E, Alegre L, Viloria TA, Remohi JA, Naranjo V, Meseguer M. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertil Steril. 2020 Dec,114(6):1232-1241. doi: 10.1016/j.fertnstert.2020.08.023 - ID:2949
Chen Z, Wang Z, Du M, Liu Z. Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review. J Ultrasound Med. 2021 Sep 15. doi: 10.1002/jum.15827 - ID:3699
Mendizabal-Ruiz G, Chavez-Badiola A, Aguilar Figueroa I, Martinez Nuño V, Flores-Saiffe Farias A, Valencia-Murilloa R, Drakeley A, Garcia-Sandoval JP, Cohen J. Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation. Reprod Biomed Online. 2022 Apr 10:S1472-6483(22)00226-7. doi: 10.1016/j.rbmo.2022.03.036 - ID:4513
Abdullah KAL, Atazhanova T, Chavez-Badiola A, Shivhare SB. Automation in ART: Paving the Way for the Future of Infertility Treatment. Reprod Sci. 2022 Aug 3. doi: 10.1007/s43032-022-00941-y - ID:4515
Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019,34(6):1011-1018. doi:10.1093/humrep/dez064 - ID:2693
Babayev E. Man versus machine in IVF-can artificial intelligence replace physicians? [published online ahead of print, 2020 Aug 17]. Fertil Steril. 2020,S0015-0282(20)30695-6. doi:10.1016/j.fertnstert.2020.07.042 - ID:2694
Jenkins J, van der Poel S, Krüssel J, et al. Empathetic application of machine learning may address appropriate utilization of ART [published online ahead of print, 2020 Jul 15]. Reprod Biomed Online. 2020,S1472-6483(20)30376-X. doi:10.1016/j.rbmo.2020.07.005 - ID:2695
