Creation of an Open-Access Lung POCUS Image Database for Deep Learning and Neural Network Applications.
Authors
Affiliations (3)
Affiliations (3)
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Anesthesiology, Stanford University School of Medicine, Stanford, CA, USA.
Abstract
Lung point of care ultrasound (POCUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray and lower cost compared to computed tomography. Despite these benefits, widespread adoption is limited by operator dependency, moderate interrater reliability, and training requirements. Deep learning (DL) could potentially address these challenges, but the development of effective algorithms is hindered by the scarcity of comprehensive image repositories with proper metadata. We created an open-source dataset of lung POCUS images derived from a multi-center study involving 226 adult patients presenting to emergency departments with respiratory symptoms between March 2020 and April 2022. Images were acquired using a standardized scanning protocol (12-zone or modified 8-zone) with various POCUS devices. Three blinded researchers independently analyzed each image following consensus guidelines, with disagreements adjudicated to provide definitive interpretations. Videos were preprocessed to remove identifiers, and frames were extracted and standardized to 512×512 pixels using letterboxing to maintain aspect ratios. The dataset contained 1,871 video clips comprising 324,027 frames extracted and standardized to 512×512 pixels. Half of the participants (50%) had COVID-19 pneumonia. Among all clips, 66% contained no abnormalities, 18% contained B-lines, 4.5% contained consolidations, 6.4% contained both B-lines and consolidations, and 5.2% had indeterminate findings. Pathological findings varied significantly by lung zone, with anterior zones more frequently normal and less likely to show consolidations compared to lateral and posterior zones. This dataset represents a large, annotated lung POCUS repository and includes patients with and without COVID-19. The repository metadata and expert interpretations enhance its utility for DL applications. Despite limitations including potential device-specific characteristics and COVID-19 predominance, this repository provides a valuable resource for developing artificial intelligence tools to improve lung POCUS acquisition and interpretation.