ArticleData in brief2025
A dataset of lung ultrasound images for automated AI-based lung disease classification.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung ultrasound (LUS) is increasingly recognized as a valuable imaging modality for evaluating various pulmonary conditions. Despite its clinical utility, accurate interpretation of LUS remains challenging due to factors such as inter-operator variability, dependence on sonographer expertise, and inherently low signal-to-noise ratios. This article presents a curated benchmark dataset of labelled LUS images acquired in Uganda, intended to support the development of automated, AI-based diagnostic tools for lung disease classification. The dataset comprises 1062 labelled images collected from patients at Mulago National Referral Hospital and Kiruddu Referral Hospital by senior radiologists. The dataset is suitable for training and evaluating convolutional neural network-based models and is expected to facilitate research in developing robust deep learning systems for pulmonary disease diagnosis using LUS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.