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.

MedImaging

Download Mobile App
Recent News Radiography MRI Ultrasound Nuclear Medicine General/Advanced Imaging Imaging IT Industry News

AI Saves One Hour of Daily Chest CT Interpretation Time for Radiologists

By MedImaging International staff writers
Posted on 14 Jun 2022
Print article
Image: AI saves one hour of daily chest CT interpretation time in prospective randomized study (Photo courtesy of Pexels)
Image: AI saves one hour of daily chest CT interpretation time in prospective randomized study (Photo courtesy of Pexels)

Incorporating artificial intelligence (AI) support into clinical practice can reduce repetitive tasks, saving approximately one hour of chest computed tomography (CT) interpretation time in a radiologist’s typical workday, according to the findings of a prospective randomized study.

For the prospective study, researchers at the Medical University of South Carolina (MUSC, Charleston, SC, USA) used the AI-Rad Companion software solution from Siemens Healthineers (Erlangen, Germany) and IMPAX 6 PACS from Agfa Healthcare (Mortsel, Belgium). The study involved 390 patients (204 female, 186 male; mean age, 62.8 years) who underwent outpatient chest CT at MUSC from January 19-28, 2021.

Siemens AI-Rad Companion provided automated analysis of cardiac, pulmonary, and musculoskeletal findings, including labeling, segmenting, and measuring normal structures, as well as detecting, labeling, and measuring abnormalities. AI-annotated images and auto-generated summary results were stored in the PACS. Chest CT examinations were randomized using 1:1 allocation between AI-assisted and non-AI arms, then clinically interpreted using a stopwatch. Ultimately, mean interpretation times were significantly shorter in the AI-assisted than in the non-AI arm for all three cardiothoracic radiologists. For readers combined, the mean difference was 93 seconds (95% CI, 63-123 seconds), corresponding with a 22.1% reduction in the AI-assisted arm: 20.0% and 24.2% for contrast-enhanced and non-contrast scans, respectively.

“This is the first study to our knowledge to assess the impact of an AI support platform on chest CT interpretation times in a real-world clinical setting,” said U. Joseph Schoepf from the MUSC. “The platform’s integration into clinical workflow resulted in a mean reduction in interpretation times of 22.1% among three cardiothoracic radiologists for whom the AI results were made available.”

“If assistance from automated AI results can save one hour of interpretation time each day as estimated from our results,” the researchers contended, “then radiologists could devote this time to other activities, whether additional clinical tasks such as communicating findings to patients and referring physicians, or administrative, education, and research responsibilities.”

Related Links:
MUSC 
Siemens Healthineers 
Agfa Healthcare 

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
X-Ray Detector
FDR-D-EVO III
New
Remote Controlled Digital Radiography and Fluoroscopy System
Eco Track-DRF - MARS 50/MARS50+/MARS 65/MARS 80
DR Flat Panel Detector
1500L

Print article

Channels

MRI

view channel
Image: uMR Jupiter 5T MRI system is the world\'s first whole-body ultra-high field MRI to officially come to market (Photo courtesy of United Imaging)

World's First Whole-Body Ultra-High Field MRI Officially Comes To Market

The world's first whole-body ultra-high field (UHF) MRI has officially come to market, marking a remarkable advancement in diagnostic radiology. United Imaging (Shanghai, China) has secured clearance from the U.... Read more

Ultrasound

view channel
Image: The powerful machine learning algorithm can “interpret” echocardiogram images and assess key findings (Photo courtesy of 123RF)

Largest Model Trained On Echocardiography Images Assesses Heart Structure and Function

Foundation models represent an exciting frontier in generative artificial intelligence (AI), yet many lack the specialized medical data needed to make them applicable in healthcare settings.... Read more

Nuclear Medicine

view channel
Image: The multi-spectral optoacoustic tomography (MSOT) machine generates images of biological tissues (Photo courtesy of University of Missouri)

New Imaging Technique Monitors Inflammation Disorders without Radiation Exposure

Imaging inflammation using traditional radiological techniques presents significant challenges, including radiation exposure, poor image quality, high costs, and invasive procedures. Now, new contrast... 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