Dataset Information

Artificial Intelligence-Augmented Pediatric Lung POCUS: A Pilot Study of Novice Learners.

ABSTRACT:

Objective

Respiratory 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.

Methods

Previously 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.

Results

Seven 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.

Conclusion

This study shows that AI-augmented lung US for diagnosing pneumonia has the potential to increase accuracy and efficiency.

SUBMITTER: Nti B

PROVIDER: S-EPMC9790545 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Artificial Intelligence-Augmented Pediatric Lung POCUS: A Pilot Study of Novice Learners.

Nti Benjamin B Lehmann Amalia S AS Haddad Aida A Kennedy Sarah K SK Russell Frances M FM

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine 20220415 12

<h4>Objective</h4>Respiratory 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 pneu ...[more]

PMID: 35429001

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Dataset Information

Artificial Intelligence-Augmented Pediatric Lung POCUS: A Pilot Study of Novice Learners.

Objective

Methods

Results

Conclusion

Publications

Artificial Intelligence-Augmented Pediatric Lung POCUS: A Pilot Study of Novice Learners.

Similar Datasets

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