Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence

Abstract

Abstract This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062–59.107), high abnormality score (OR, 1.081; 95% CI, 1.066–1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003–1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509–8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55–10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients’ factors, image-acquisition protocols, and the presence of concurrent lesions on CXR.

Keywords

• Pneumothorax
• Artificial intelligence
• Lung
• Software
• Predictive value of tests