ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2022
Artificial Intelligence-Augmented Pediatric Lung POCUS: A Pilot Study of Novice Learners.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- Role of point-of-care ultrasonography in kidney disease management: A single solution for multiple challenges.World journal of nephrology · 2026Article
- Creation of an Open-Access Lung POCUS Image Database for Deep Learning and Neural Network Applications.POCUS journal · 2026Article
- The application of artificial intelligence in blind ultrasound sweep diagnostics for prenatal medicine: A systematic literature review.Acta obstetricia et gynecologica Scandinavica · 2026Review
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Point-of-Care Kidney Ultrasonography in Pediatric Nephrology: Current Evidence and Clinical Relevance.International journal of nephrology · 2026Review
- Review
- Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children.PLOS digital health · 2025Review
- Trust of Artificial Intelligence-Augmented Point-of-Care Ultrasound Among Pediatric Emergency Physicians.Journal of the American College of Emergency Physicians open · 2025Article
- Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).The ultrasound journal · 2025Article
- Overcoming barriers in the use of artificial intelligence in point of care ultrasound.NPJ digital medicine · 2025Review
- Limited echocardiogram acquisition by novice clinicians aided with deep learning: A randomized controlled trial.Biology methods & protocols · 2025Article
- Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients.Journal of personalized medicine · 2024Review
- Article
- Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.Anaesthesiology intensive therapy · 2024Review
- Modern Paediatric Emergency Department: Potential Improvements in Light of New Evidence.Children (Basel, Switzerland) · 2023Review
- Emergency Ultrasound: Is It Time for Artificial Intelligence?Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveRespiratory symptoms are among the most common chief complaints of pediatric patients in the emergency department (ED). Point-of-care ultrasound (POCUS) outperforms conventional chest X-ray and is user-dependent, which can be challenging to novice ultrasound (US) users. We introduce a novel concept using artificial intelligence (AI)-enhanced pleural sweep to generate complete panoramic views of the lungs, and then assess its accuracy among novice learners (NLs) to identify pneumonia.
methodsPreviously healthy 0- to 17-year-old patients presenting to a pediatric ED with cardiopulmonary chief complaint were recruited. NLs received a 1-hour training on traditional lung POCUS and the AI-assisted software. Two POCUS-trained experts interpreted the images, which served as the criterion standard. Both expert and learner groups were blinded to each other's interpretation, patient data, and outcomes. Kappa was used to determine agreement between POCUS expert interpretations.
resultsSeven NLs, with limited to no prior POCUS experience, completed examinations on 32 patients. The average patient age was 5.53 years (±1.07). The median scan time of 7 minutes (minimum-maximum 3-43; interquartile 8). Three (8.8%) patients were diagnosed with pneumonia by criterion standard. Sensitivity, specificity, and accuracy for NLs AI-augmented interpretation were 66.7% (confidence interval [CI] 9.4-99.1%), 96.5% (CI 82.2-99.9%), and 93.7% (CI 79.1-99.2%). The average image quality rating was 2.94 (±0.16) out of 5 across all lung fields. Interrater reliability between expert sonographers was high with a kappa coefficient of 0.8.
conclusionThis study shows that AI-augmented lung US for diagnosing pneumonia has the potential to increase accuracy and efficiency.
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