ArticleBMC medical informatics and decision making2023
Automatic deep learning-based pleural effusion segmentation in lung ultrasound images.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Artificial intelligence for lung ultrasound interpretation: a systematic review.Frontiers in radiology · 2026Pooled it
- Multimodal Diagnostic Ultrasound for Congestion, Perfusion, and Ultrafiltration Tolerance in Maintenance Hemodialysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- OPT-former: A transformer-based approach for eye motion tracking in ocular proton therapy.Medical physics · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- A deep learning model based on ultrasound imaging to differentiate malignant from benign pleural effusion: a multicenter cohort study.Respiratory research · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Incremental predictive value of a CT-based deep learning radiomics model for differentiating benign and malignant pleural effusions.Frontiers in medicine · 2026Article
- Computer vision-based support system for B-line detection in lung ultrasound.Frontiers in digital health · 2026Article
- Advances in bedside imaging: lung ultrasound.Intensive care medicine experimental · 2025Review
- AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.JAMA cardiology · 2025Article
- Diagnostic Value of Bedside Lung Ultrasound and Chest CT on Subpleural Lesions and Lung Consolidation in ICU Patients with Severe Pneumonia.International journal of general medicine · 2025Article
- Convolutional automatic identification of B-lines and interstitial syndrome in lung ultrasound images using pre-trained neural networks with feature fusion.Frontiers in digital health · 2025Article
- Harnessing AI for Improved Detection and Classification of Pleural Effusion: Insights and Innovations.Canadian respiratory journal · 2025Review
- Ultrasound quantification of pleural effusion volume in supine position: comparison of three model formulae.BMC pulmonary medicine · 2024Article
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPoint-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigation and treatment. LUS typically requires training and experience from the clinician to accurately interpret the images. To address this limitation, we previously demonstrated a deep-learning model capable of detecting the presence of PE on LUS at an accuracy greater than 90%, when compared to an experienced LUS operator.
methodsThis follow-up study aimed to develop a deep-learning model to provide segmentations for PE in LUS. Three thousand and forty-one LUS images from twenty-four patients diagnosed with PE were selected for this study. Two LUS experts provided the ground truth for training by reviewing and segmenting the images. The algorithm was then trained using ten-fold cross-validation. Once training was completed, the algorithm segmented a separate subset of patients.
resultsComparing the segmentations, we demonstrated an average Dice Similarity Coefficient (DSC) of 0.70 between the algorithm and experts. In contrast, an average DSC of 0.61 was observed between the experts.
conclusionIn summary, we showed that the trained algorithm achieved a comparable average DSC at PE segmentation. This represents a promising step toward developing a computational tool for accurately augmenting PE diagnosis and treatment.
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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 Socratic graph.