ArticleJournal of applied clinical medical physics2024
Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.
Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- A Lightweight Network for Free Fluid Detection in Focused Assessment with Sonography in Trauma (FAST) Examination.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence for pleural effusion and pneumothorax detection on thoracic ultrasound: an educational viewpoint on the promise, pitfalls and path forward.Breathe (Sheffield, England) · 2026Article
- Artificial Intelligence-Based Automated Analysis for Pleural Effusion Detection on Thoracic Ultrasound: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence models for point-of-care ultrasound diagnostics in dogs.Frontiers in veterinary science · 2026Article
- 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
- Ultrasound findings of free fluid in non-traumatic acute abdomen: a prospective case series study.BMC surgery · 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
- Automated Analysis of Ultrasound for the Diagnosis of Pneumothorax: A Systematic Review.Cureus · 2024Review
- Ultrasound quantification of pleural effusion volume in supine position: comparison of three model formulae.BMC pulmonary medicine · 2024Article
- Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network.Brain sciences · 2024Article
- Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images.Bioengineering (Basel, Switzerland) · 2024Article
- Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.Journal of applied clinical medical physics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundUltrasonic for detecting and evaluating pleural effusion is an essential part of the Extended Focused Assessment with Sonography in Trauma (E-FAST) in emergencies. Our study aimed to develop an Artificial Intelligence (AI) diagnostic model that automatically identifies and segments pleural effusion areas on ultrasonography.
methodsAn Attention U-net and a U-net model were used to detect and segment pleural effusion on ultrasound images of 848 subjects through fully supervised learning. Sensitivity, specificity, precision, accuracy, F1 score, the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) were used to assess the model's effectiveness in classifying the data. The dice coefficient was used to evaluate the segmentation performance of the model.
resultsIn 10 random tests, the Attention U-net and U-net 's average sensitivity of 97% demonstrated that the pleural effusion was well detectable. The Attention U-net performed better at identifying negative images than the U-net, which had an average specificity of 91% compared to 86% for the U-net. Additionally, the Attention U-net was more accurate in predicting the pleural effusion region because its average dice coefficient was 0.86 as opposed to the U-net's average dice coefficient of 0.82.
conclusionsThe Attention U-net showed excellent performance in detecting and segmenting pleural effusion on ultrasonic images, which is expected to enhance the operation and application of E-FAST in clinical work.
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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.