Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection in computed tomography of the chest

• Published in: Clinical imaging 2023-09, Vol.101, p.50-55
• Main Authors: Salman, Rida, Nguyen, HaiThuy N., Sher, Andrew C., Hallam, Kristina A., Seghers, Victor J., Sammer, Marla B.K.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Lung Computer Aided Detection; Lung nodules; Pediatric
• ISSN: 0899-7071; 1873-4499
• DOI: 10.1016/j.clinimag.2023.05.019

To test the performance of a commercially available adult pulmonary nodule detection artificial intelligence (AI) tool in pediatric CT chests. 30 consecutive chest CTs with or without contrast of patients ages 12–18 were included. Images were retrospectively reconstructed at 3 mm and 1 mm slice thickness. AI for detection of lung nodules in adults (Syngo CT Lung Computer Aided Detection (CAD)) was evaluated. 3 mm axial images were retrospectively reviewed by two pediatric radiologists (reference read) who determined the location, type, and size of nodules. Lung CAD results at 3 mm and 1 mm slice thickness were compared to reference read by two other pediatric radiologists. Sensitivity (Sn) and positive predictive value (PPV) were analyzed. The radiologists identified 109 nodules. At 1 mm, CAD detected 70 nodules; 43 true positive (Sn = 39 %), 26 false positive (PPV = 62 %), and 1 nodule which had not been identified by radiologists. At 3 mm, CAD detected 60 nodules; 28 true positive (Sn = 26 %), 30 false positive (PPV = 48 %) and 2 nodules which had not been identified by radiologists. There were 103 solid nodules (47 measuring