Work

Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device

Year: 2020

Type: preprint

Abstract: Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively. In this work, we propose a low-cost one-dimensional Doppler-based method for estimating gestational age (GA). Doppler time series were collected from 401 pregnancies between 5 and 9 months GA using a smartphone. The proposed model for GA estimation is based on sequence learning by forming a temporally dependent model using a convolutional long-short-term memory network. Time-frequency features are extracted from Doppler signals and regularized before feeding to the network. The overall mean absolute GA error with respect to the last menstrual period was found to be 0.71 month, which outperforms all previous works.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2012.00553

Authors: Katebi, Nasim, Sameni, Reza, Clifford, Gari D.

Institution: Emory University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, Fogarty International Center, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development