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
Radcal IBA  Group

Download Mobile App





AI Predicts COVID Prognosis at Near-Expert Level Based Off CT Scans

By HospiMedica International staff writers
Posted on 05 Apr 2022

A new study to evaluate the ability of a chest CT deep learning algorithm for quantification of COVID-19 lung disease found that it was highly predictive of inpatient outcomes and performed at a near expert level. More...

Investigators at the Medical University of South Carolina (Charleston, SC, USA) evaluated the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia. For the study, a previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest CT scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.

The study revealed that interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). The probability of each outcome behaved as a logistic function of the opacity scoring (25% ICU admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). The study also found that the length of hospitalization, ICU stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively). Based on these findings, the researchers concluded that the tested dCNN was highly predictive of inpatient outcomes, can perform at a near expert level, and provide added value for clinicians in terms of prognostication and disease severity.

“The use of artificial intelligence deep learning models to prognosticate from CT images has been identified from the beginning of the pandemic as a potential way to expedite the triage process, improve prognostication, and guideline utilization of resources,” explained corresponding author U. Joseph Schoepf, MD, from the Division of Cardiovascular Imaging at the Medical University of South Carolina in Charleston. “Utilizing AI severity scoring may be helpful in meeting the challenge of practical, reproducible triage of COVID-19 patients by identifying patients at high risk for morbidity and mortality.”

Related Links:
Medical University of South Carolina


Gold Member
12-Channel ECG
CM1200B
Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
New
Diagnostic Ultrasound System
DC-80A
New
Half Apron
Demi
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to HospiMedica.com and get access to news and events that shape the world of Hospital Medicine.
  • Free digital version edition of HospiMedica International sent by email on regular basis
  • Free print version of HospiMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of HospiMedica International in digital format
  • Free HospiMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Surgical Techniques

view channel
Image: The milli-spinner can shrink blood clots without rupturing them (Photo courtesy of Andrew Brodhead/Stanford)

New Technology More Than Doubles Success Rate for Blood Clot Removal

In cases of ischemic stroke, where a blood clot obstructs oxygen supply to the brain, time is critical. The faster the clot is removed and blood flow restored, the more brain tissue can be saved, improving... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.