Published November 27, 2024 | Version 1.0.0
Dataset Open

NatalIA: PBF-US1 (Phantom Blind-sweeps for Fetal Ultrasound Scanning)

Description

NatalIA PBF-US1 is dataset designed to support the development of AI-based tools for detecting relevant fetal planes in ultrasound videos captured by non-trained personnel, such as midwives or nurses.

Abstract

In low-income countries, particularly in remote communities with a shortage of trained sonographers and high maternal mortality rates, developing AI tools to assist non-experts in accurately identifying relevant fetal planes and potential anomalies during ultrasound exams is crucial. This dataset includes 19407 ultrasound frames collected from 90 videos of a 23-week gestational age fetal ultrasound phantom, recorded through free-hand sweeps by non-experts. These data are valuable for advancing research on second-trimester pregnancies in low-income areas with untrained personnel. The frames were extracted from videos captured using a point-of-care ultrasound (POCUS) device in obstetric mode, set to a maximum depth of 16 cm. In total, 45 volunteers with no prior ultrasound experience recorded the videos while following four predefined scanning paths: vertical, horizontal, and two diagonal trajectories, with four different fetal poses. This approach creates a dataset that reflects real-world variability in non-expert settings, simulating ultrasound exams conducted by untrained personnel.

Methods

The dataset was collected using the US-7a SPACE FAN phantom (Kyoto Kagaku, Japan) in conjunction with a Clarius C3 HD3 point-of-care ultrasound (POCUS) device (Clarius, Canada). This phantom simulates a pregnancy at 23 weeks of gestation. 

Videos were acquired with the POCUS device, capturing at a maximum depth of 16 cm and at 24 frames per second. To gather all the collected videos, a total of 45 volunteers were asked to perform four different sweeps: vertical, horizontal, and two diagonal trajectories. In total, 90 videos were acquired, resulting in 19407 frames, which will be detailed in the tables below.

Videos

Sweep # of videos Position # of videos
Vertical

21

Occiput Posterior (OP) 34
Horizontal 27 Sacrum Posterior (SP) 26
Diagonal 1 22 Occiput Anterior (OA) 15
Diagonal 2 20 Sacrum Anterior (SA) 15

Frames

Plane # of planes Plane # of planes
Biparietal Plane (Value: 0) 42 Abdominal Plane (Value: 1) 63
Heart Plane (Value: 2) 61 Spine Plane (Value: 3) 134
Femur Plane (Value: 4) 46 No Plane (Value: 5) 19061

 

Image annotation was performed by a radiologist, an obstetrician, and pre-trained medical students using the Labelbox platform (Labelbox, USA). Labels were subject to a review process for annotations performed by medical students, where the radiologist validated them.

 

Notes

For more information: NatalIA-PBF-US1

This project was funded by CLIAS (Centro de Inteligencia Artificial y Salud para América Latina y el Caribe), an initiative of CIIPS (Centro de Implementación e Innovación de Políticas de Salud) at IECS (Instituto de Efectividad Clínica y Sanitaria), with support from IDRC (International Development Research Centre).

Files

NatalIA PBFUS1.zip

Files (266.3 MB)

Name Size Download all
md5:c679bbea00ee120e793e809c58ff7de7
266.3 MB Preview Download

Additional details

Funding

Instituto de Efectividad Clínica y Sanitaria

Software

Repository URL
https://github.com/BiomedLabUGgt/NatalIA-PBF-US1
Programming language
Python