Objectives Accounts of initial and follow-up chest X-rays (CXRs) of the Middle East respiratory coronavirus (MERS-CoV) patients, and correlation with outcomes, are sparse. We retrospectively evaluated MERS-CoV CXRs initial findings, temporal progression, and outcomes correlation. Materials and methods Fifty-three real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR)-confirmed MERS-CoV patients with CXRs were retrospectively identified from November 2013 to October 2014. Initial and follow-up CXR imaging findings and distribution were evaluated over 75 days. Findings were correlated with outcomes. Results Twenty-two of 53 (42%) initial CXRs were normal. In 31 (68%) abnormal initial CXRs, 15 (48%) showed bilateral non-diffuse involvement, 16 (52%) had ground-glass opacities (GGO), and 13 (42%) had peripheral distribution. On follow-up CXRs, mixed airspace opacities prevailed, seen in 16 (73%) of 22 patients 21-30 days after the initial CXRs. Bilateral non-diffuse involvement was the commonest finding throughout follow-up, affecting 16 (59%) of 27 patients 11-20 days after the initial CXRs. Bilateral diffuse involvement was seen in five (63%) of eight patients 31-40 days after the initial CXRs. A bilateral diffuse CXR pattern had an odds ratio for mortality of 13 (95% CI=2-78) on worst and 18 (95% CI=3-119) on final CXRs (P-value <0.05). Conclusion Initially, normal CXRs are common in MERS-CoV patients. Peripherally located ground-glass and mixed opacities are common on initial and follow-up imaging. The risk of mortality is higher when bilateral diffuse radiographic abnormalities are detected.
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http://dx.doi.org/10.7759/cureus.24860 | DOI Listing |
Int J Cardiovasc Imaging
January 2025
Shanxi Cardiovascular Hospital, 18 Yifen Street, Taiyuan, 030024, Shanxi, China.
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
November 2024
The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States.
Purpose: This study aimed to investigate the impact of different model retraining schemes and data partitioning on model performance in the task of COVID-19 classification on standard chest radiographs (CXRs), in the context of model generalizability.
Approach: Two datasets from the same institution were used: Set A (9860 patients, collected from 02/20/2020 to 02/03/2021) and Set B (5893 patients, collected from 03/15/2020 to 01/01/2022). An original deep learning (DL) model trained and tested in the task of COVID-19 classification using the initial partition of Set A achieved an area under the curve (AUC) value of 0.
J Am Coll Radiol
November 2024
Professor, Department of Family Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea.
Sci Rep
October 2024
Department of Anesthesiology, DaChien Health Medical System, Miaoli, Taiwan.
Chest X-rays (CXRs) are primarily used to detect lung lesions. While the abdominal portion of CXRs can sometimes reveal critical conditions, research in this area is limited. To address this, we introduce a two-stage architecture that separates the abdominal region from the CXR and detects abdominal lesions using a specialized dataset.
View Article and Find Full Text PDFBMJ Open
September 2024
Digital Health Validation Lab, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
Introduction: Diagnosing and treating lung cancer in early stages is essential for survival outcomes. The chest X-ray (CXR) remains the primary screening tool to identify lung cancers in the UK; however, there is a shortfall of radiologists, while demand continues to increase. Image analysis by machine-learning software has the potential to support radiology workflows with a focus on immediate triage of suspicious X-rays.
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