# Lung Ultrasound Imaging Dataset for Accurate Detection and Localization of LUS Vertical Artifact

## TL;DR

This paper introduces a new dataset of lung ultrasound images annotated for vertical artifacts, aiming to improve automated detection and diagnosis of respiratory conditions.

## Contribution

The novel contribution is a curated dataset of 401 annotated LUS images focused on vertical artifacts, collected from Ugandan hospitals.

## Key findings

- A dataset of 401 high-resolution LUS images annotated with polygonal bounding boxes was created.
- The dataset includes images from 152 patients with pulmonary conditions at two Ugandan hospitals.
- The dataset is intended to support the development of deep learning models for automated LUS interpretation.

## Abstract

Lung ultrasound (LUS) vertical artifacts are critical sonographic markers commonly used in evaluating pulmonary conditions such as pulmonary edema, interstitial lung disease, pneumonia, and COVID-19. Accurate detection and localization of these artifacts are vital for informed clinical decision-making. However, interpreting LUS images remains highly operator-dependent, leading to variability in diagnosis. While deep learning (DL) models offer promising potential to automate LUS interpretation, their development is limited by the scarcity of annotated datasets specifically focused on vertical artifacts. This study introduces a curated dataset of 401 high-resolution LUS images, each annotated with polygonal bounding boxes to indicate vertical artifact locations. The images were collected from 152 patients with pulmonary conditions at Mulago and Kiruddu National Referral Hospitals in Uganda. This dataset serves as a valuable resource for training and evaluating DL models designed to accurately detect and localize LUS vertical artifacts, contributing to the advancement of AI-driven diagnostic tools for early detection and monitoring of respiratory diseases.