Machine learning to study placental pathology: Risk of reification and other considerations-Reference-Cited by-同舟云学术

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Machine learning to study placental pathology: Risk of reification and other considerations

Published:2023-02-15 Issue:4 Volume:37 Page:362-364
ISSN:0269-5022
Container-title:Paediatric and Perinatal Epidemiology
language:en
Short-container-title:Paediatric Perinatal Epid

Author:

Cartus Abigail R.¹^ORCID

Affiliation:

1. Department of Epidemiology Brown University School of Public Health Providence Rhode Island USA

Funder

National Institute on Drug Abuse

Publisher

Wiley

Subject

Pediatrics, Perinatology and Child Health,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ppe.12961

Reference10 articles.

1. Maternal-fetal conditions necessitating a medical intervention resulting in preterm birth

2. Placental histology in spontaneous and indicated preterm birth: A case control study

3. Placental pathology in pregnancies complicated by fetal growth restriction: recurrence vs. new onset

4. Review: Preeclampsia, acute atherosis of the spiral arteries and future cardiovascular disease: Two new hypotheses

5. Machine Learning for Fetal Growth Prediction

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automated detection of microscopic placental features indicative of maternal vascular malperfusion using machine learning;Placenta;2024-01

2. Automated Detection of Maternal Vascular Malperfusion Lesions of the Placenta using Machine Learning;2023-06-27

3. The data giveth, but what do we take away?;Paediatric and Perinatal Epidemiology;2023-04-03

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