ArticleDiagnostics (Basel, Switzerland)2021
Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for lung ultrasound interpretation: a systematic review.Frontiers in radiology · 2026Pooled it
- MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique.Journal of imaging informatics in medicine · 2026Article
- Lung ultrasound interpretation using deep learning for the detection of B-lines in dogs.Journal of veterinary internal medicine · 2026Article
- The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review.NPJ primary care respiratory medicine · 2026Article
- Computer vision-based support system for B-line detection in lung ultrasound.Frontiers in digital health · 2026Article
- Semi-supervised Ensemble Learning for Automatic Interpretation of Lung Ultrasound Videos.Journal of imaging informatics in medicine · 2025Article
- AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).The ultrasound journal · 2025Article
- Practical Applications of Lung and Diaphragm Ultrasound in the Intensive Care Unit: An Updated Narrative Review.Cureus · 2025Review
- Ten Questions on Using Lung Ultrasonography to Diagnose and Manage Pneumonia in the Hospital-at-Home Model: Part I-Techniques and Patterns.Diagnostics (Basel, Switzerland) · 2024Review
- Thoracic Ultrasound in Others Scenarios: An Expanding Tool.Open respiratory archives · 2024Review
- Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.Scientific reports · 2024Article
- Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.Journal of medical Internet research · 2024Article
- Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.Diagnostics (Basel, Switzerland) · 2024Article
- The value of lung ultrasound score in neonatal respiratory distress syndrome: a prospective diagnostic cohort study.Frontiers in medicine · 2024Article
- Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit.Critical care (London, England) · 2023Article
- Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.BMC pulmonary medicine · 2023Article
- Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees.Canadian medical education journal · 2023Article
- New International Guidelines and Consensus on the Use of Lung Ultrasound.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2023Review
- Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.
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