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
GLOBETECH PUBLISHING LLC

Download Mobile App




AI Identifies Noncancerous Thyroid Nodules on Ultrasound Images and Reduces Biopsies

By MedImaging International staff writers
Posted on 13 Jun 2022
Print article
Image: AI can be used to identify benign thyroid nodules and reduce unnecessary biopsies (Photo courtesy of Pexels)
Image: AI can be used to identify benign thyroid nodules and reduce unnecessary biopsies (Photo courtesy of Pexels)

Thyroid nodules are very common. Fine needle aspiration biopsy is used to diagnose thyroid cancer. However most biopsies produce benign (non-cancerous) results and are potentially avoidable. Now, a new study has found that artificial intelligence (AI) can be used to identify thyroid nodules seen on thyroid ultrasound that are very unlikely to be cancerous, reducing a large number of unnecessary biopsies.

In the new study, researchers at the University of Colorado Anschutz Medical Campus (Aurora, CO, USA) used machine learning, a type of AI, to analyze ultrasound images of thyroid nodules. Machine learning is the process of using mathematical models of data to help a computer learn without direct instruction. More than 30,000 images from 621 thyroid nodules were used to train the machine-learning model that classifies thyroid nodules as “cancer” or “no cancer.” The model was tested on a different set of 145 nodules collected at another healthcare system. The AI-based model achieved a sensitivity (ability to not miss cancer) of 97%, and a specificity (ability to correctly identify a cancer) of 61%.

“This study demonstrates that the ultrasound-based AI classifier of thyroid nodules achieves sensitivity comparable to that of thyroid biopsy with fine needle aspiration,” said study lead researcher Nikita Pozdeyev, M.D., Ph.D., of the University of Colorado Anschutz Medical Campus.

“We believe this is a good next step to improving patient care and avoiding unnecessary procedures,” he said. He noted that prospective clinical trials are needed before this tool can be accepted as a standard of care.

“We demonstrated that using AI analysis of ultrasound images to rule out thyroid cancer and avoid biopsy is definitely possible,” he said. “This technology could assist radiologists and endocrinologists in choosing which thyroid nodules should undergo biopsy, especially those in the community who may not review a large number of thyroid ultrasound images.”

Related Links:
University of Colorado Anschutz Medical Campus 

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Ultrasound Doppler System
Doppler BT-200
New
X-Ray QA Meter
Piranha CT
New
Enterprise Imaging & Reporting Solution
Syngo Carbon

Print article
Radcal

Channels

MRI

view channel
Image: The emerging role of MRI alongside PSA testing is redefining prostate cancer diagnostics (Photo courtesy of 123RF)

Combining MRI with PSA Testing Improves Clinical Outcomes for Prostate Cancer Patients

Prostate cancer is a leading health concern globally, consistently being one of the most common types of cancer among men and a major cause of cancer-related deaths. In the United States, it is the most... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... 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 © 2000-2024 Globetech Media. All rights reserved.