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We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.
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http://dx.doi.org/10.1371/journal.pdig.0000057 | DOI Listing |
Acad Radiol
October 2024
Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH (K.B., N.S., S.M.A.B., C.M., D.C., B.R., A.G.). Electronic address:
Rationale And Objectives: Artificial intelligence (AI) algorithms in radiology capable of detecting urgent findings have gained significant traction in recent years, but the impact of these algorithms on real-world clinical practice remains unclear with need for scientific investigation. Our study investigates the diagnostic accuracy and impact on radiologist report turnaround times of an FDA-approved AI tool for pneumothorax (PTx) detection on inpatient chest X-rays (CXR) in our institution's radiology practice at a large academic medical center.
Materials And Methods: This retrospective study included 27,397 frontal, single-view CXRs of adult inpatients collected consecutively between August 2020 and April 2021 following deployment of an AI-based PTx detection and picture archiving and communication system (PACS) alert system.
BJR Open
January 2024
Qure.ai, Mumbai, India.
Objectives: Artificial intelligence (AI) enabled devices may be able to optimize radiologists' productivity by identifying normal and abnormal chest X-rays (CXRs) for triaging. In this service evaluation, we investigated the accuracy of one such AI device (qXR).
Methods: A randomly sampled subset of general practice and outpatient-referred frontal CXRs from a National Health Service Trust was collected retrospectively from examinations conducted during November 2022 to January 2023.
Front Med (Lausanne)
March 2024
Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan.
Introduction: Physical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment.
View Article and Find Full Text PDFMed Image Learn Ltd Noisy Data (2023)
October 2023
Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
We proposed a self-supervised machine learning method to automatically rate the severity of pulmonary edema in the frontal chest X-ray radiographs (CXR) which could be potentially related to COVID-19 viral pneumonia. For this we use the modified radiographic assessment of lung edema (mRALE) scoring system. The new model was first optimized with the simple Siamese network (SimSiam) architecture where a ResNet-50 pretrained by ImageNet database was used as the backbone.
View Article and Find Full Text PDFJ Thorac Imaging
January 2024
Department of Radiology.
Purpose: After intubation, a frontal chest radiograph (CXR) is obtained to assess the endotracheal tube (ETT) position by measuring the ETT tip-to-carina distance. ETT tip location changes with neck position and can be determined by assessing the position of the mandible. As the mandible is typically not visualized on standard CXRs, we developed a new protocol where the mandible is seen on the CXR, hypothesizing that it will improve the accuracy of the ETT position assessment.
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