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Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs

 Hyun Joo Shin  ;  Nak-Hoon Son  ;  Min Jung Kim  ;  Eun-Kyung Kim 
 SCIENTIFIC REPORTS, Vol.12(1) : 10215, 2022-06 
Journal Title
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Adolescent ; Adult ; Artificial Intelligence* ; Cardiomegaly ; Child ; Child, Preschool ; Humans ; Pneumothorax* ; Radiography ; Radiography, Thoracic / methods ; Retrospective Studies ; Sensitivity and Specificity
Artificial intelligence (AI) applied to pediatric chest radiographs are yet scarce. This study evaluated whether AI-based software developed for adult chest radiographs can be used for pediatric chest radiographs. Pediatric patients (≤ 18 years old) who underwent chest radiographs from March to May 2021 were included retrospectively. An AI-based lesion detection software assessed the presence of nodules, consolidation, fibrosis, atelectasis, cardiomegaly, pleural effusion, pneumothorax, and pneumoperitoneum. Using the pediatric radiologist's results as standard reference, we assessed the diagnostic performance of the software. For the total 2273 chest radiographs, the AI-based software showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 67.2%, 91.1%, 57.7%, 93.9%, and 87.5%, respectively. Age was a significant factor for incorrect results (odds radio 0.821, 95% confidence interval 0.791-0.851). When we excluded cardiomegaly and children 2 years old or younger, sensitivity, specificity, PPV, NPV and accuracy significantly increased (86.4%, 97.9%, 79.7%, 98.7% and 96.9%, respectively, all p < 0.001). In conclusion, AI-based software developed with adult chest radiographs showed diagnostic accuracies up to 96.9% for pediatric chest radiographs when we excluded cardiomegaly and children 2 years old or younger. AI-based lesion detection software needs to be validated in younger children.
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1. College of Medicine (의과대학) > Dept. of Pediatrics (소아과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Min Jung(김민정) ORCID logo https://orcid.org/0000-0002-5634-9709
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Shin, Hyun Joo(신현주) ORCID logo https://orcid.org/0000-0002-7462-2609
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