We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




X-Ray Imaging May Be Inadequate to Diagnose COVID-19

By MedImaging International staff writers
Posted on 27 Apr 2020
Print article
A new study finds that among cases of confirmed COVID-19 patients, almost 90 % of chest x-rays read as normal or mild.

Researchers at Ohio State University (OSU, Columbus, USA), the State University of New York (SUNY, USA), and other institutions conducted a study involving 636 ambulatory patients (57% male) in New York who tested positive for SARS-CoV-2, and who also underwent a chest x-ray during March 2020. Eleven board-certified radiologists--who knew they were reading images from COVID-19 patients--reviewed the chest x-rays and classified the findings as normal, mild, moderate, or severe disease. The patients ranged in age from 18 to 90, and over three-quarters were 30-70 years of age.

The results revealed that 58% of the chest x-rays were classified as normal. Of the 42% that did not read as normal, 195 demonstrated mild disease, 65 had moderate disease, and only five had severe disease. The most common findings were interstitial changes (23.7%) and ground glass opacities (19%). About a third of the findings were in the lower lobe, a quarter were multifocal, and about 21% were bilateral. Both effusions and lymphadenopathy were uncommon. The researchers noted that 74% of the original readings were classified as normal, but that 97 were changed to abnormal when read for the study. The study was published in the May 2020 issue of the Journal of Urgent Care Medicine.

“Providers ordering a chest x-ray in the outpatient setting should be aware that a patient with symptoms of COVID-19 may have a negative chest x-ray, and should manage the patient based on their symptoms,” said lead author Michael Weinstock, MD, of the OSU Wexner Medical Center. “Because most COVID-19 patients do not have severe illness, this is likely the population clinicians will be seeing, where their symptoms are severe enough to seek care. Doctor’s should not be reassured by a negative chest x-ray.”

The authors added that limitations to the data include its retrospective and observational nature, and that only a single chest x-ray series was obtained for each patient, making it impossible to determine if patients developed radiographic findings as the illness progressed. They also noted a lack of underlying health histories or basic chest x-rays to identify chronic pulmonary conditions. Finally, the radiologists were not blinded to the fact the patients had been diagnosed with COVID-19, which may have impacted their classification of the images.

Related Links:
Ohio State University
State University of New York


Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Ultrasound Doppler System
Doppler BT-200
Laptop Ultrasound Scanner
PL-3018
New
Ceiling-Mounted Digital Radiography System
Radiography 5000 C

Print article
Radcal

Channels

MRI

view channel
Image: PET/MRI can accurately classify prostate cancer patients (Photo courtesy of 123RF)

PET/MRI Improves Diagnostic Accuracy for Prostate Cancer Patients

The Prostate Imaging Reporting and Data System (PI-RADS) is a five-point scale to assess potential prostate cancer in MR images. PI-RADS category 3 which offers an unclear suggestion of clinically significant... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.