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Congress: ECR24
Poster Number: C-17087
Type: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2024/C-17087
Authorblock: F. Souschek, P. Mildenberger, L. Müller, T. Jorg, M. C. Halfmann; Mainz/DE
Disclosures:
Fabio Souschek: Nothing to disclose
Peter Mildenberger: Nothing to disclose
Lukas Müller: Nothing to disclose
Tobias Jorg: Nothing to disclose
Moritz Christian Halfmann: Nothing to disclose
Keywords: Artificial Intelligence, Thorax, Plain radiographic studies, Comparative studies, Quality assurance
Methods and materials

In this retrospective study, a commercially available AI tool analysed 151 supine chest X-rays from ICU patients and 70 PA view chest X-rays from patients at a tertiary care hospital (Figure 1).

Fig 1: Example of an AI tool report of a supine chest X-ray from an ICU patient
Reports from board-certified radiologists served as a reference standard. Diagnostic accuracies for detecting lung opacities, consolidation, pleural effusion, pulmonary edema, pneumothorax, and cardiomegaly were compared by means of sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV).
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