Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus.

ABSTRACT:

Introduction

Placental parenchymal lesions are commonly encountered and carry significant clinical associations. However, they are frequently missed or misclassified by general practice pathologists. Interpretation of pathology slides has emerged as one of the most successful applications of machine learning (ML) in medicine with applications ranging from cancer detection and prognostication to transplant medicine. The goal of this study was to use a whole-slide learning model to identify and classify placental parenchymal lesions including villous infarctions, intervillous thrombi (IVT), and perivillous fibrin deposition (PVFD).

Methods

We generated whole slide images from placental discs examined at our institution with infarct, IVT, PVFD, or no macroscopic lesion. Slides

SUBMITTER: Goldstein JA 

PROVIDER: S-EPMC10156426 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature