External validation based on transfer learning for diagnosing atelectasis using portable chest X-rays

BACKGROUND: Although there has been a large amount of research focusing on medical image classification, few studies have focused specifically on the portable chest X-ray. To determine the feasibility of transfer learning method for detecting atelectasis with portable chest X-ray and its application to external validation, based on the analysis of a large dataset. METHODS: From the intensive care chest X-ray medical information market (MIMIC-CXR) database, 14 categories were obtained using natural language processing tags, among which 45,808 frontal chest radiographs were labeled as “atelectasis,” and 75,455 chest radiographs labeled “no finding.” A total of 60,000 images were extracted, including positive images labeled “atelectasis” and positive X-ray images labeled “no finding.” The data were categorized into “normal” and “atelectasis,” which were evenly distributed and randomly divided into three cohorts (training, validation, and testing) at a ratio of about 8:1:1. This retrospective study extracted 300 X-ray images labeled “atelectasis” and “normal” from patients in ICUs of The First Affiliated Hospital of Jinan University, which was labeled as an external dataset for verification in this experiment. Data set performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive values derived from transfer learning training. RESULTS: It took 105 min and 6 s to train the internal training set. The AUC, sensitivity, specificity, and accuracy were 88.57, 75.10, 88.30, and 81.70%. Compared with the external validation set, the obtained AUC, sensitivity, specificity, and accuracy were 98.39, 70.70, 100, and 86.90%. CONCLUSION: This study found that when detecting atelectasis, the model obtained by transfer training with sufficiently large data sets has excellent external verification and acculturate localization of lesions.