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

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images. Photo by Greg Freiherr

Feature | Artificial Intelligence | April 19, 2019 | By Greg Freiherr
Commercial efforts to develop...
Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
Videos | Breast Imaging | April 18, 2019
In a keynote lecture at the Society of Breast Imaging (SBI)/American College of Radiology (ACR) 2019 Symposium, ...
Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer

Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer. Permission to publish provided by DenseBreast-info.org

Feature | Breast Imaging | April 18, 2019 | By Greg Freiherr
When planning a screening program to detect the early signs of breast cancer, age is a major consideration.
Oxipit Introduces Multilingual Support for ChestEye AI Imaging Suite
News | Artificial Intelligence | April 16, 2019
The CE-certified ChestEye artificial intelligence (AI) imaging suite by Oxipit is now available in seven European...
iCAD Appoints Stacey Stevens as President
News | Radiology Business | April 16, 2019
iCAD Inc. recently announced that Stacey Stevens has been named president. As president, Stevens will have expanded...
compressed breast during mammography.
360 Photos | 360 View Photos | April 16, 2019
A 360 view of a simulated breast compression for a...
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. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.