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 Tools Increase Low-Dose CT Lung Nodule Specificity

By MedImaging International staff writers
Posted on 02 Feb 2021
Image:  AI identification of lung nodule matches or bests that of trained radiologists (Photo courtesy of iStock)
Image: AI identification of lung nodule matches or bests that of trained radiologists (Photo courtesy of iStock)
Combining artificial intelligence (AI) and lung imaging reporting and data system (Lung-RADS) scores can increase CT scan specificity without reducing sensitivity, according to a new study.

Researchers at the University of Saskatchewan (Saskatoon, Canada) conducted a study that performed secondary analysis of a known data set using an AI model developed by Google in 2019, and Lung-RADS classifications from six radiologists. They then compared them to assess a representative cohort of 3,197 baseline low-dose CT screening patients. To ensure the AI algorithm matched the 91% sensitivity level achieved by the providers, the researchers determined a 0.27 AI risk-score threshold, based on a 0-to-1 scale.

The results showed that the AI-informed management strategy achieved sensitivity and specificity of 91% and 96%, respectively, while the average sensitivity and specificity of the six radiologists using only Lung-RADS was 91% and 61%, respectively. Based on the AI management strategy, 0.2% of category 1 or 2 Lung-RADS classifications were upgraded to category 3, and 30% of category 3 or higher classifications were downgraded to category 2. The minimum net cost savings, based on 2019 U.S. Medicare physician fee schedule, was USD 72 per patient screened. The study was published on January 19, 2021, in Journal of the American College of Radiology.

“Using an AI risk score combined with Lung-RADS at baseline lung cancer screening may result in fewer follow-up investigations and substantial cost savings. Specificity could rise by more than fifty percent,” concluded lead author Scott Adams, MD, and colleagues. “Additional research for other AI thresholds could also beneficial, especially for Lung-RADS category 4 nodules. Ultimately, additional investigations could lead to AI algorithms being used in a similar way to what has been suggested for screening mammography.”

Lung-RADS is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations, reduce confusion in lung cancer screening CT interpretations, and facilitate outcome monitoring. It is modeled on the success of the Breast Imaging Reporting and Data System (BI-RADS), with the primary goal of minimizing variation in the management of CT-detected lung nodules so that screening can be implemented effectively in radiology practices.

Related Links:
University of Saskatchewan

New
Ultrasound Needle Guidance System
SonoSite L25
New
Breast Localization System
MAMMOREP LOOP
Digital Intelligent Ferromagnetic Detector
Digital Ferromagnetic Detector
Ultrasound Table
Women’s Ultrasound EA Table

Channels

Ultrasound

view channel
Image: The new implantable device for chronic pain management is small and flexible (Photo courtesy of The Zhou Lab at USC)

Wireless Chronic Pain Management Device to Reduce Need for Painkillers and Surgery

Chronic pain affects millions of people globally, often leading to long-term disability and dependence on opioid medications, which carry significant risks of side effects and addiction.... Read more

Nuclear Medicine

view channel
Image: The diagnostic tool could improve diagnosis and treatment decisions for patients with chronic lung infections (Photo courtesy of SNMMI)

Novel Bacteria-Specific PET Imaging Approach Detects Hard-To-Diagnose Lung Infections

Mycobacteroides abscessus is a rapidly growing mycobacteria that primarily affects immunocompromised patients and those with underlying lung diseases, such as cystic fibrosis or chronic obstructive pulmonary... 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.