Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise

We show that various abnormalities of villous maturation and morphology correlate with abnormalities in placental age. This "no pathologist" model could be useful in situations where pathologists are unavailable or the need for consistency outweighs the utility of expertise.

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Publication Details

Journal
Archives of Pathology & Laboratory Medicine
Published
2024-10-06
DOI
https://doi.org/10.5858/arpa.2024-0274-oa
Citations
4
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
2.33
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article

Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise

Ramin Nateghi, Lee Cooper, Jeffrey A. Goldstein
4 citations
Archives of Pathology & Laboratory Medicine
Pregnancy and preeclampsia studies
2.33
article

Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise

Ramin Nateghi, Lee Cooper, Jeffrey A. Goldstein
article en
4 citations

Abstract

We show that various abnormalities of villous maturation and morphology correlate with abnormalities in placental age. This "no pathologist" model could be useful in situations where pathologists are unavailable or the need for consistency outweighs the utility of expertise.

Archives of Pathology & Laboratory MedicineVol. 149(6)
Northwestern University (US), McCormick (United States) (US)
Openalex Percentile: Top 11%
Pregnancy and preeclampsia studies
2.33
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