CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

Autor:	Irvin JA; Department of Computer Science., Pareek A; AIMI Center, Stanford University, Stanford., Long J; AIMI Center, Stanford University, Stanford., Rajpurkar P; Department of Computer Science., Eng DK; AIMI Center, Stanford University, Stanford.; Bunkerhill Health, Palo Alto, CA., Khandwala N; AIMI Center, Stanford University, Stanford.; Bunkerhill Health, Palo Alto, CA., Haug PJ; Care Transformations Department, Intermountain Healthcare.; Department of Biomedical Informatics., Jephson A; Division of Pulmonary and Critical Care Medicine., Conner KE; Department of Radiology, Intermountain Medical Center, Salt Lake City, UT., Gordon BH; Department of Radiology, Intermountain Medical Center, Salt Lake City, UT., Rodriguez F; Department of Radiology, Intermountain Medical Center, Salt Lake City, UT., Ng AY; Department of Computer Science., Lungren MP; AIMI Center, Stanford University, Stanford., Dean NC; Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah.; Division of Pulmonary and Critical Care Medicine.
Jazyk:	angličtina
Zdroj:	Journal of thoracic imaging [J Thorac Imaging] 2022 May 01; Vol. 37 (3), pp. 162-167. Date of Electronic Publication: 2021 Sep 23.
DOI:	10.1097/RTI.0000000000000622
Abstrakt:	Purpose: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs. Materials and Methods: In this retrospective cohort study, a dataset of 7434 prior chest radiographic studies from 6551 ED patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against 3 radiologists' adjudicated interpretation and compared with performance of the natural language processing of radiology reports used by ePNa. Results: The deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% confidence interval [CI]: 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI: 0.911, 0.962) for detecting pleural effusions and 0.847 (95% CI: 0.800, 0.890) for identifying multilobar pneumonia. On all 3 tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared with ePNa. Conclusions: A deep learning model demonstrated higher agreement with radiologists than the ePNa CDSS in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia CDSS could enhance diagnostic performance and improve pneumonia management. Competing Interests: The authors declare no conflicts of interest. (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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