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. 2024 Dec 28;14(1):31329.
doi: 10.1038/s41598-024-82775-z.

Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points

Affiliations

Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points

Hyun Joo Shin et al. Sci Rep. .

Abstract

The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients under 19 years old, collected between March and November 2021, were divided into test and exploring sets. A commercial adult-oriented AI software was utilized to detect lung lesions, including pneumothorax, consolidation, nodule, and pleural effusion, using a standard operating point of 15%. A pediatric radiologist reviewed the radiographs to establish ground truth for lesion presence. To determine the optimal operating points, receiver operating characteristic (ROC) curve analysis was conducted, varying thresholds to balance sensitivity and specificity by lesion type, age group, and imaging method. The test set (4,727 chest radiographs, mean 7.2 ± 6.1 years) and exploring set (2,630 radiographs, mean 5.9 ± 6.0 years) yielded optimal operating points of 11% for pneumothorax, 14% for consolidation, 15% for nodules, and 6% for pleural effusion. Using a 3% operating point improved pneumothorax sensitivity for children under 2 years, portable radiographs, and anteroposterior projections. Therefore, optimizing operating points of AI based on lesion type, age, and imaging method could improve diagnostic performance for pediatric chest radiographs, building on adult-oriented AI as a foundation.

Keywords: Artificial intelligence; Child; Pneumothorax; ROC curve; Radiologists.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Actual example of AI results for pneumothorax and pleural effusion. (a) A 17-year-old boy presented with a right pneumothorax (arrow) and a small amount of bilateral pleural effusion (arrowheads). However, the AI software did not correctly detect these findings because the operating points for pneumothorax and pleural effusion were 11% and 6%, respectively. (b) However, AI accurately identified the right pneumothorax (abbreviated as Ptx) with an operating point of 97% on his initial radiograph.
Fig. 2
Fig. 2
ROC curves for pneumothorax detection in patients aged 2 years and under in (a) test and (b) exploring sets. Sensitivities were improved by applying the new optimal operating point (blue) compared to the conventional operating point of 15% (red). The specificity and sensitivity are presented in parentheses.

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