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

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.