 |
|
Artificial intelligence in obstetrics
Ki Hoon Ahn, Kwang-Sig Lee
Obstet Gynecol Sci. 2022;65(2):113-124. Published online 2021 Dec 15 DOI: https://doi.org/10.5468/ogs.21234
|
|
Citations to this article as recorded by
 How Automated Techniques Ease Functional Assessment of the Fetal Heart: Applicability of MPI+™ for Direct Quantification of the Modified Myocardial Performance Index
Jann Lennard Scharf, Christoph Dracopoulos, Michael Gembicki, Amrei Welp, Jan Weichert
Diagnostics.2023; 13(10): 1705. CrossRef Machine learning and disease prediction in obstetrics
Zara Arain, Stamatina Iliodromiti, Gregory Slabaugh, Anna L. David, Tina T. Chowdhury
Current Research in Physiology.2023; 6: 100099. CrossRef Predicting a clinically narrow pelvis using neural network data analysis
A. M. Ziganshin, G. B. Dikke, V. A. Mudrov
Obstetrics, Gynecology and Reproduction.2023; 17(2): 211. CrossRef A research study on the cervical cerclage to deal with cervical insufficiency using machine learning
Mandeep Kaur, Ganesh Khedkar, Sachin Sakhare, Katarina Rogulj
Soft Computing.2023;[Epub] CrossRef A deep learning mixed-data type approach for the classification of FHR signals
Edoardo Spairani, Beniamino Daniele, Maria Gabriella Signorini, Giovanni Magenes
Frontiers in Bioengineering and Biotechnology.2022;[Epub] CrossRef Vorteile der KI-gestützten geburtshilflichen Überwachung
O. Graupner, C. Enzensberger
Die Gynäkologie.2022; 55(10): 740. CrossRef Aktuelle Aspekte zur künstlichen Intelligenz in der gynäkologischen Diagnostik
Jann Lennard Scharf, Christoph Dracopoulos, Michael Gembicki, Amrei Welp, Jan Weichert
Die Gynäkologie.2022; 55(10): 759. CrossRef
|