Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest radiograph

Abstract

Abstract Background Pulmonary arterial hypertension is a severe condition. Despite advances in targeted treatments for it, diagnostic delays have yet to improve. Early referral to a specialized hospital is important because a longer diagnostic interval has been reported to be associated with decreased 5-year survival. Computer-aided detection and diagnosis (CAD) support detecting and diagnosing abnormalities and diseases. In this study, we developed and tested a deep-learning algorithm to detect pulmonary arterial hypertension using chest radiographs. Methods From the image archive of Chiba University Hospital, 259 chest radiographs from 145 patients with pulmonary arterial hypertension PAH and 260 radiographs from 260 control patients were identified; 418 were used for training, and 101 were used for testing. Using the testing dataset for each image, the algorithm outputted a numerical value from zero to one (the probability of the pulmonary arterial hypertension score). In addition, using the same testing dataset, the capability of the algorithm to identify pulmonary arterial hypertension was compared with that of professional doctors. Results The area under the curve of the receiver operating characteristic curve for the detection ability of the algorithm was 0.988. Using a score cutoff of 0.69, the sensitivity and specificity of the algorithm were 0.933 and 0.982, respectively. The performance of the algorithm was not inferior to that of the doctors. Conclusion We developed a deep-learning algorithm to detect pulmonary arterial hypertension using chest radiographs.