AI-enabled workflow for automated classification and analysis of feto-placental Doppler images.

ABSTRACT:

Introduction

Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors.

Methods

To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurat

SUBMITTER: Aguado AM 

PROVIDER: S-EPMC11521966 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature