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 hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




AI Chest CT Analysis Distinguishes COVID-19 from Pneumonia

By MedImaging International staff writers
Posted on 31 Mar 2020
Image: Heatmaps of COVID-19 activation regions (Photo courtesy of Wuhan Huangpi People\'s Hospital)
Image: Heatmaps of COVID-19 activation regions (Photo courtesy of Wuhan Huangpi People\'s Hospital)
An artificial intelligence (AI) deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia (CAP) and other lung diseases, according to a new study.

Researchers at Wuhan Huangpi People's Hospital (China), Shenzhen University Health Science Center (Shenzhen, China), and other institutions conducted a multicenter, retrospective study to develop the COVID-19 detection neural network (COVNet), a fully automatic AI framework that extracts visual features from volumetric chest CT exams for the detection of COVID-19, and differentiates them from CAP and other lung findings. Datasets were collected from six hospitals and diagnostic performance was assessed and evaluated.

In all, the collected dataset consisted of 4,356 volumetric chest CT exams from 3,322 patients (average age 49 years, 55% male). Overall per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 90% and 96%, respectively. The researchers also showed that the per-exam sensitivity and specificity for detecting CAP was 87% and 92%, respectively. The study was published on March 19, 2020, in Radiology.

“We were able to collect a large number of CT exams from multiple hospitals, which included 1,296 COVID-19 CT exams,” concluded lead author Lin Li, MD, of the Wuhan Huangpi People's Hospital department of radiology. “More importantly, 1,735 CAP and 1,325 non-pneumonia CT exams were also collected as the control groups in this study in order to ensure the detection robustness, considering that certain similar imaging features may be observed in COVID-19 and other types of lung diseases.”

The coronavirus Disease 2019 (COVID-19) outbreak has rapidly spread all over the world. On January 30, 2020, it was declared as a public health emergency of international concern by the World Health Organization (WHO). It is typically confirmed by reverse-transcription polymerase chain reaction (PCR), but CT can detect certain characteristic manifestations in the lung associated with COVID-19, such as bilateral ground-glass, consolidative pulmonary opacities on CT, and a crazy-paving pattern, among others.

Related Links:
Shenzhen University Health Science Center

Breast Localization System
MAMMOREP LOOP
Post-Processing Imaging System
DynaCAD Prostate
Diagnostic Ultrasound System
DC-80A
Digital Intelligent Ferromagnetic Detector
Digital Ferromagnetic Detector

Channels

Nuclear Medicine

view channel
Image: Perovskite crystal boules are grown in carefully controlled conditions from the melt (Photo courtesy of Mercouri Kanatzidis/Northwestern University)

New Camera Sees Inside Human Body for Enhanced Scanning and Diagnosis

Nuclear medicine scans like single-photon emission computed tomography (SPECT) allow doctors to observe heart function, track blood flow, and detect hidden diseases. However, current detectors are either... 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 © 2000-2025 Globetech Media. All rights reserved.