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

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

AI Improves Lung Nodule Detection on Chest X-Rays

By MedImaging International staff writers
Posted on 08 Feb 2023
Print article
Image: AI-based software significantly improves detection of lung nodules on chest X-rays (Photo courtesy of Pexels)
Image: AI-based software significantly improves detection of lung nodules on chest X-rays (Photo courtesy of Pexels)

Lung nodules are common abnormal growths that typically form on the lungs due to previous lung infections but can rarely be a sign of lung cancer. Chest X-ray is a common screening method used to identifying lung nodules. Artificial intelligence (AI) can serve as a powerful tool to help identify lung nodules, particularly when radiologists have a high volume of cases. Now, a pioneering, randomized controlled study evaluating the effect of AI-based software in real clinical practice has found that AI significantly improved the detection of lung nodules on chest X-rays.

In order to identify the actual effect that AI has in clinical practice, researchers at Seoul National University Hospital (Seoul, Korea) conducted a study involving 10,476 patients with an average age of 59 years, who had undergone chest X-rays at a health screening center between June 2020 and December 2021. Patients were also asked to complete a self-reported health questionnaire for identifying baseline characteristics such as age, sex, smoking status and previous history of lung cancer. Within the group of patients, 11% were current or former smokers. The researchers randomly divided the patients evenly into two groups - AI or non-AI. Radiologists aided by AI analyzed the X-rays of the first group while the X-rays of the second group were interpreted without using AI.

Solid nodules with diameters either larger than 8 millimeters or subsolid nodules with a solid portion larger than six millimeters were identified as actionable, meaning that the nodule required follow-up based on lung cancer screening criteria. The researchers identified lung nodules in 2% of the patients. Their analysis showed that the detection rate for actionable lung nodules on chest X-rays was higher when aided by AI (0.59%) as compared to without AI assistance (0.25%). They found no differences in the false-referral rates between the AI and non-AI interpreted groups.

Older age and a history of lung cancer or tuberculosis were associated with positive reports, although these and other health characteristics did not impact the efficacy of the AI system. This indicates that AI can perform consistently across different populations, including those with diseased or postoperative lungs. The researchers now plan to conduct a similar study using chest CT which will also identify clinical outcomes and efficiency of workflow.

"Our study provided strong evidence that AI could really help in interpreting chest radiography. This will contribute to identifying chest diseases, especially lung cancer, more effectively at an earlier stage," said study co-author Jin Mo Goo, M.D., Ph.D., from the Department of Radiology at Seoul National University Hospital.

Related Links:
Seoul National University Hospital 

Gold Supplier
Ultrasound Transducer/Probe Cleaner
Transeptic Cleaning Solution
New
X-Ray System
Straight Arm
New
Silver Supplier
Step Platform for U-Arm System
U-Arm Step
New
Barrier Mount
RayShield SideWinder

Print article

Channels

MRI

view channel
Image: New research harnesses the power of machine learning in prostate cancer imaging (Photo courtesy of UMiami Health System)

Machine Learning Aids Diagnosis and Prognosis of Prostate Cancer Using MRI

Conventional magnetic resonance imaging (MRI) is a reliable tool for prognosis, diagnosis, active surveillance, and reducing the need for biopsy procedures in lower-risk prostate cancer patients.... Read more

Ultrasound

view channel
Image: New focused ultrasound is effective for treating Parkinson’s, movement disorders (Photo courtesy of Pexels)

New Focused Ultrasound Treatment Proves Effective for Parkinson’s Disease Patients

Parkinson's disease is a neurological condition characterized by the loss of dopamine neurons within the brain. While medications such as levodopa can be effective in managing this condition, some patients... Read more

Nuclear Medicine

view channel
Image: Tracking radiation treatment in real time promises safer, more effective cancer therapy (Photo courtesy of Pexels)

Real-Time 3D Imaging Provides First-of-Its-Kind View of X-Rays Hitting Inside Body During Radiation Therapy

Radiation is used in treatment for hundreds of thousands of cancer patients each year, bombarding an area of the body with high energy waves and particles, usually X-rays. The radiation can kill cancer... 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 © 2000-2023 Globetech Media. All rights reserved.