Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms

• Published in: Korean journal of radiology Vol. 26; no. 9; pp. 877 - 887
• Main Authors: Rhee, Chanyoung, Hong, Ki Jeong, Kim, Ki Hong, Goo, Jin Mo, Hwang, Eui Jin
• Format: Journal Article
• Language: English
• Published: 대한영상의학회 01.09.2025
• Subjects: 방사선과학
• ISSN: 1229-6929; 2005-8330
• DOI: 10.3348/kjr.2025.0237

Objective: In this study, we investigated whether artificial intelligence (AI) analysis of chest radiographs (CXRs) can predict major adverse clinical events in patients visiting the emergency department (ED) with acute cardiopulmonary symptoms. Materials and Methods: This secondary analysis of a previous clinical trial included patients who visited the ED with symptoms suggestive of acute cardiopulmonary disease and underwent chest radiography between June 2020 and December 2021. All patients underwent triage upon arrival at ED according to the Korean Triage and Acuity Scale (KTAS). The CXRs were retrospectively analyzed using a commercial AI (Lunit INSIGHT CXR, version 3.1.4.1) capable of detecting seven abnormalities on a single frontal CXR. The predictive performance of the AI analysis for major adverse cardiopulmonary events (any among hospitalization, ED revisits, and death in the ED due to acute cardiopulmonary disease) was compared with that of the KTAS using the area under the receiver operating characteristic curve (AUC). Multivariable (the AI analysis result and KTAS level) logistic regression analysis was conducted to investigate whether the AI analysis result was an independent predictor of the events and whether the combination of the AI analysis and KTAS has additional merit. Results: Among 3576 patients (1966 males; mean age, 64 years), 1148 (32.1%) experienced major adverse cardiopulmonary events. AI analysis of CXRs outperformed the KTAS in predicting these events (AUC, 0.795 vs. 0.610; P < 0.001). The AI analysis result was an independent predictor of these events after adjusting for the KTAS level (adjusted odd ratios of 1.032 and 6.913 for every 1% increase and ≥15%, respectively, in the AI probability score; P < 0.001). The combination of the AI analysis and KTAS showed an AUC that was higher than that of the KTAS alone (0.799; P < 0.001) and in-par with that of the AI analysis only (P = 0.187). Conclusion: AI analysis of CXRs showed greater accuracy than the KTAS did in predicting major adverse cardiopulmonary events in patients visiting the ED with acute cardiopulmonary symptoms. AI analysis may enhance the efficacy of patient triage in the ED. KCI Citation Count: 0