Technology | Computer-Aided Detection Software | March 27, 2017

iCAD Receives FDA Approval for PowerLook Tomo Detection

Deep learning technology improves efficiency and reduces reading time on digital breast 3-D tomosynthesis for radiologists

iCAD, PowerLook Tomo Detection, computer-aided detection software, CAD, digital breast tomosynthesis, DBT, RSNA 2017

March 27, 2017 — iCAD announced that PowerLook Tomo Detection received Premarket Approval (PMA) from the U.S. Food and Drug Administration (FDA). PowerLook Tomo Detection is a concurrent-read computer-aided detection (CAD) solution for digital breast 3-D tomosynthesis, available on the PowerLook Breast Health Solutions platform. 

Two-dimensional digital mammography typically produces four images per exam while digital breast 3-D tomosynthesis can produce hundreds of images, significantly increasing exam interpretation time for radiologists. PowerLook Tomo Detection improves radiologists’ efficiency by automatically analyzing each tomosynthesis plane and identifying suspicious areas. The suspicious areas are naturally blended onto a 2-D synthetic image to provide radiologists with a single enhanced image that is used to more efficiently navigate the large tomosynthesis data set. 

“iCAD has taken a refreshing new approach to computer-aided detection. This innovative workflow solution detects suspicious areas on the tomosynthesis planes and that information is used to deliver an enhanced image that focuses the radiologist on the specific areas that need further investigation,” said Justin Boatsman, M.D., medical director and diagnostic radiologist, Intrinsic Imaging LLC, who took part in the U.S. clinical study. “This not only helps reduce the reading time and improve the reading experience for radiologists, but it can also provide radiologists with an added level of confidence.” 

In a U.S., clinical study conducted from October 2015 to January 2016, radiologists were able to significantly reduce reading time when reading 3-D tomosynthesis exams with PowerLook Tomo Detection. The study included 20 radiologists reading 240 tomosynthesis cases, both with and without the PowerLook Tomo Detection technology. Reading time was reduced by up to 37 percent with an average reduction of 29 percent when using PowerLook Tomo Detection, with no statistically significant impact on sensitivity, specificity or recall rate.

Another European clinical study was completed with six radiologists reading 80 cases and showed similar results and was the basis for CE Mark of PowerLook Tomo Detection in April 2016. 

The application employs deep learning, a branch of machine learning that uses sophisticated algorithms that are trained to recognize the visual characteristics of a cancer by analyzing actual patient images. The current version of the algorithm was trained using thousands of images. 

PowerLook Tomo Detection, currently available on GE Healthcare digital breast tomosynthesis systems, also received CE Mark and Health Canada approval in 2016, and is currently being used by multiple high volume breast imaging centers in Europe.

For more information: www.icadmed.com

Related Content

FDA Clears GE Healthcare's Critical Care Suite Chest X-ray AI
Technology | X-Ray | September 12, 2019
GE Healthcare announced the U.S. Food and Drug Administration’s (FDA) 510(k) clearance of Critical Care Suite, a...
iCAD's ProFound AI Wins Best New Radiology Solution in 2019 MedTech Breakthrough Awards
News | Computer-Aided Detection Software | September 09, 2019
iCAD Inc. announced MedTech Breakthrough, an independent organization that recognizes the top companies and solutions...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used deep learning, a form of machine learning, which is a type of artificial intelligence. Image courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Society of Breast Imaging (SBI) | September 06, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Global Diagnostics Australia Incorporates AI Into Radiology Applications
News | Artificial Intelligence | September 04, 2019
Global Diagnostics Australia (GDA), a subsidiary of the Integral Diagnostics Group (IDX), has adopted artificial...
Ashley County Medical Center Installs Arkansas' First Fujifilm Aspire Cristalle With DBT
News | Mammography | August 27, 2019
Fujifilm Medical Systems U.S.A. Inc. recently announced that Ashley County Medical Center (Crossett, Ark.) has invested...
FDA Encourages Inclusion of Male Patients in Breast Cancer Clinical Trials
News | Women's Health | August 26, 2019
The U.S. Food and Drug Administration (FDA) released a new draft guidance that encourages including male patients in...
Moffitt Researchers Develop Model to Personalize Breast Cancer Radiation Treatment
News | Radiation Therapy | August 26, 2019
A personalized approach to cancer treatment has become more common over the last several decades, with numerous...
Sectra Signs Enterprise Imaging Contract With Vanderbilt Health
News | Enterprise Imaging | August 21, 2019
Sectra will install its enterprise imaging picture archiving and communication system (PACS) and vendor neutral archive...
Some Pregnant Women Are Exposed to Gadolinium in Early Pregnancy
News | Women's Health | August 20, 2019
A small but concerning number of women are exposed to a commonly used magnetic resonance imaging (MRI) contrast agent...
Lunit Receives Korea MFDS Approval for Lunit Insight MMG
News | Artificial Intelligence | August 19, 2019
Lunit has announced Korea Ministry of Food and Drug Safety (MFDS) approval of its artificial intelligence (AI) solution...