Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

• Published in: Pediatric radiology Vol. 52; no. 11; pp. 2120 - 2130
• Main Authors: Schalekamp, Steven, Klein, Willemijn M., van Leeuwen, Kicky G.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-10-2022; Springer Nature B.V
• Subjects: Abnormalities; Adults; Artificial intelligence; Artificial Intelligence in Pediatric Radiology; Bacterial diseases; Bone density; Chest; Computed tomography; Cystic fibrosis; Deep learning; Disease; Imaging; Lung cancer; Medicine; Medicine & Public Health; Metastasis; Neuroradiology; Nuclear Medicine; Oncology; Pediatrics; Pneumonia; Pneumothorax; Population; Radiography; Radiology; Software; Thorax; Tuberculosis; Ultrasound; Thorax; Children; Computed tomography; Artificial intelligence; Chest radiography; Pediatric radiology
• ISSN: 0301-0449; 1432-1998
• DOI: 10.1007/s00247-021-05146-0

Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.