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

Hyun Joo Shin¹, Kyunghwa Han², Nak-Hoon Son³

¹Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, 16995, Gyeonggi-do, Republic of Korea.

Scientific Reports

|December 29, 2024

Summary

Optimizing artificial intelligence (AI) operating points for pediatric chest X-rays improves diagnostic accuracy. Tailoring AI thresholds by lesion type and patient factors enhances detection of lung abnormalities in children.

Keywords:

Artificial intelligence Child Pneumothorax ROC curve Radiologists

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Area of Science:

Radiology
Artificial Intelligence
Pediatric Imaging

Background:

Adult-oriented artificial intelligence (AI) software is increasingly used in medical imaging.
The diagnostic performance of AI may vary when applied to pediatric populations due to anatomical and physiological differences.

Purpose of the Study:

To evaluate if optimal operating points for adult AI software differ in pediatric chest radiographs.
To assess the diagnostic performance of AI in pediatric chest imaging after optimizing operating points.

Main Methods:

A commercial adult AI software was tested on pediatric chest radiographs (under 19 years).
Receiver operating characteristic (ROC) curve analysis was used to determine optimal operating points for different lesion types (pneumothorax, consolidation, nodule, pleural effusion).
Ground truth was established by a pediatric radiologist.

Main Results:

Optimal operating points varied by lesion type: 11% for pneumothorax, 14% for consolidation, 15% for nodules, and 6% for pleural effusion.
A 3% operating point specifically improved pneumothorax sensitivity in younger children (<2 years), portable X-rays, and anteroposterior views.
The study utilized large datasets of 4,727 test and 2,630 exploring pediatric chest radiographs.

Conclusions:

Optimizing AI operating points based on lesion type, patient age, and imaging method is crucial for enhancing diagnostic performance in pediatric chest radiography.
Adult-oriented AI can serve as a foundation, but requires specific adjustments for effective pediatric application.