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

ABSTRACT:

Context.—

Assessment of placental villous maturation is among the most common tasks in perinatal pathology. However, the significance of abnormalities in morphology is unclear and interobserver variability is significant.

Objective.—

To develop a machine learning model of placental maturation across the second and third trimesters and quantify the impact of different pathologist-diagnosed abnormalities of villous morphology.

Design.—

Digitize placental villous slides from more than 2500 placentas at 12.0 to 42.6 weeks. Build whole slide learning models to estimate obstetrician-determined gestational age for cases with appropriate maturation and normal morphology. Define the model output as "placental age" and compare it to the chronologic gestational age.

Results.—

SUBMITTER: Goldstein JA 

PROVIDER: S-EPMC12306973 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature