Deep learning (DL) analysis of chest radiographs may improve triage of patients with acute chest pain (ACP) syndrome in the ED,
according to a study published in Radiology. Márton Kolossváry, MD, PhD, and colleagues examined whether a DL analysis of initial chest radiographs can help triage patients with ACP syndrome. To predict the 30-day composite endpoint—including acute coronary syndrome, pulmonary embolism, or aortic dissection, and all-cause mortality—a DL model was trained on 23,005 patients based on chest radiographs. Performance between models was compared using the area under the receiver operating characteristic curve (model 1: age + sex; model 2: model 1 + conventional troponin or d-dimer positivity; model 3: model 2 + DL prediction). Compared with models 1 and 2, model 3—which included DL predictions—significantly improved discrimination of those with the composite outcome. Of patients, 14% could be deferred from testing for differential diagnosis of ACP syndrome using model 3 versus 2% using model 2, with a sensitivity threshold of 99%.