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Cited by in CrossRef
For: Miyagi Y, Habara T, Hirata R, Hayashi N. Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters. Artif Intell Med Imaging 2020; 1(3): 94-107 [DOI: 10.35711/aimi.v1.i3.94]
URL: https://www.wjgnet.com/2644-3260/full/v1/i3/94.htm
Number Citing Articles
1
Jerónimo Hernández-González, Olga Valls, Adrián Torres-Martín, Jesús Cerquides. Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical modelsComputers in Biology and Medicine 2022; 150: 106160 doi: 10.1016/j.compbiomed.2022.106160
2
Nina Dissler, Daniela Nogueira, Bertrand Keppi, Pierre Sanguinet, Christophe Ozanon, Cendrine Geoffroy-Siraudin, Xavier Pollet-Villard, Alexandra Boussommier-Calleja. Artificial intelligence-powered assisted ranking of sibling embryos to increase first cycle pregnancy rateReproductive BioMedicine Online 2024; 49(1): 103887 doi: 10.1016/j.rbmo.2024.103887
3
Hang Liu, Zhuoran Zhang, Yifan Gu, Changsheng Dai, Guanqiao Shan, Haocong Song, Daniel Li, Wenyuan Chen, Ge Lin, Yu Sun. Development and evaluation of a live birth prediction model for evaluating human blastocysts from a retrospective studyeLife 2023; 12 doi: 10.7554/eLife.83662
4
Krittapat Onthuam, Norrawee Charnpinyo, Kornrapee Suthicharoenpanich, Supphaset Engphaiboon, Punnarai Siricharoen, Ronnapee Chaichaowarat, Chanakarn Suebthawinkul. Combined Input Deep Learning Pipeline for Embryo Selection for In Vitro Fertilization Using Light Microscopic Images and Additional FeaturesJournal of Imaging 2025; 11(1): 13 doi: 10.3390/jimaging11010013
5
Yasunari Miyagi, Toshiyuki Hata, Saori Bouno, Aya Koyanagi, Takahito Miyake. Recognition of facial expression of fetuses by artificial intelligence (AI)Journal of Perinatal Medicine 2021; 49(5): 596 doi: 10.1515/jpm-2020-0537
6
Shanshan Wang, Lei Chen, Haixiang Sun. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort studyReproductive BioMedicine Online 2023; 47(6): 103371 doi: 10.1016/j.rbmo.2023.103371
7
Yulia Michailov, Shevach Friedler, Bozhena Saar-Ryss. Methods to improve frozen-thawed blastocyst transfer outcomes- the IVF laboratory perspectiveJournal of IVF-Worldwide 2023; 1(1-3) doi: 10.46989/001c.87541
8
Yasunari Miyagi, Yasuyuki Mio, Keitaro Yumoto, Rei Hirata, Toshihiro Habara, Nobuyoshi Hayashi. Kinetic Energy and the Free Energy Principle in the Birth of Human LifeReproductive Medicine 2024; 5(2): 65 doi: 10.3390/reprodmed5020008
9
Yasunari Miyagi, Toshihiro Habara, Rei Hirata, Nobuyoshi Hayashi. Predicting implantation by using dual AI system incorporating three‐dimensional blastocyst image and conventional embryo evaluation parameters—A pilot studyReproductive Medicine and Biology 2024; 23(1) doi: 10.1002/rmb2.12612
10
Mikkel Fly Kragh, Henrik Karstoft. Embryo selection with artificial intelligence: how to evaluate and compare methods?Journal of Assisted Reproduction and Genetics 2021; 38(7): 1675 doi: 10.1007/s10815-021-02254-6
11
Toshiyuki Hata, Yasunari Miyagi. Recognition of Fetal Facial Expressions Using Artificial Intelligence Deep LearningDonald School Journal of Ultrasound in Obstetrics and Gynecology 2021; 15(3): 223 doi: 10.5005/jp-journals-10009-1710
12
R. Barkavi, G. Yamuna, C. Jayaram. Proceedings of International Conference on Communication and Computational TechnologiesAlgorithms for Intelligent Systems 2023; : 947 doi: 10.1007/978-981-99-3485-0_76
13
Aşina Bayram, Ibrahim Elkhatib, Erkan Kalafat, Andrea Abdala, Virginia Ferracuti, Laura Melado, Barbara Lawrenz, Human Fatemi, Daniela Nogueira. Steady morphokinetic progression is an independent predictor of live birth: a descriptive reference for euploid embryosHuman Reproduction Open 2024; 2024(4) doi: 10.1093/hropen/hoae059
14
Sergei Sergeev, Iuliia Diakova, Lasha Nadirashvili. Neural networks pipeline for quality management in IVF laboratoryJournal of IVF-Worldwide 2024; 2(4) doi: 10.46989/001c.124947