Validation of a deep learning model for classification of pediatric pneumonia in Hong Kong

Validation of a deep learning model for classification of pediatric pneumonia in Hong Kong

•A deep learning model was developed to screen pediatric chest x-rays for pneumonia.•The model automatically classifies chest x-rays as positive or negative for pneumonia.•Model was validated by comparing its output to classification from trained evaluators.•Model has high accuracy, sensitivity and...

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Veröffentlicht in:Vaccine 2024-12, Vol.42 (26), p.126370, Article 126370
Hauptverfasser: Wang, Dong, Ru, Boshu, Lee, Elaine Yuen Phin, Hwang, Andy Cheuk Nam, Chan, Kate Ching-Ching, Weaver, Jessica, White, Meghan, Chen, Yiyun, Lao, Kim S.J., Khan, Tsz K., Roberts, Craig S.
Format: Artikel
Sprache:eng
Schlagworte:
Agreements
Algorithms
Artificial intelligence
Automation
Classification
Datasets
Deep learning
Image interpretation, computer-assisted
Independent sample
Mortality
Observer variation
Patients
Pediatrics
Pneumococcal infections
Pneumonia
Pneumonia/classification
Radiography, thoracic
Streptococcus infections
Vaccines
X-rays
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Beschreibung
Zusammenfassung:•A deep learning model was developed to screen pediatric chest x-rays for pneumonia.•The model automatically classifies chest x-rays as positive or negative for pneumonia.•Model was validated by comparing its output to classification from trained evaluators.•Model has high accuracy, sensitivity and specificity compared to human interpretation.•Cropping the images improved the model's classification performance.
ISSN:0264-410X
1873-2518
1873-2518
DOI:10.1016/j.vaccine.2024.126370