News | Computer-Aided Detection Software | November 10, 2017

Kansas City Radiology Center Employs iCAD PowerLook Tomo Detection for DBT

Technology utilizes advanced artificial intelligence to improve speed and accuracy in the detection of breast cancer

Kansas City Radiology Center Employs iCAD PowerLook Tomo Detection for DBT

November 10, 2017 — Imaging for Women, a leading radiology center in Kansas City, Mo., announced it is now using iCAD’s PowerLook Tomo Detection, an advanced artificial intelligence (AI) technology, to support faster and more accurate detection of breast cancer. PowerLook Tomo Detection is a concurrent-read, cancer detection solution for 3-D digital breast tomosynthesis (DBT).

In a U.S. clinical study conducted in 2016, radiologists were able to decrease reading time 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.

“At Imaging for Women, we are dedicated to bringing our patients and community the most advanced technologies available today to support breast cancer detection,” said Phyllis Fulk, administrator, Imaging for Women. “With iCAD’s PowerLook Tomo Detection, we are now able to take screening and diagnostic mammography to the next level, making it possible to improve detection with greater accuracy and more efficiency, without compromising clinical performance.”

Unlike 2D full-field digital mammography that typically produces four images per breast exam, tomosynthesis exams produce hundreds of images, often requiring radiologists to spend a significant amount of time reviewing and interpreting images. iCAD’s PowerLook Tomo Detection utilizes a trained algorithm developed through deep learning that automatically analyzes each plane in this vast data set, supporting radiologists in identifying questionable areas with greater speed and precision. Suspicious areas identified are then blended into a 2-D synthetic image to provide radiologists with a single enhanced image.

“Our radiology team is thrilled with the unique capabilities and improved workflow iCAD’s AI breast cancer detection technology provides, said Mark Malley, M.D., chief of radiology, Imaging for Women. “I have a higher level of security that I haven’t missed any significant findings with the iCAD for tomosynthesis. This is a helpful tool in our diagnostic toolbox.”

For more information: www.icadmed.com

Related Content

Varian Unveils Ethos Solution for Adaptive Radiation Therapy
News | Image Guided Radiation Therapy (IGRT) | September 16, 2019
At the 2019 American Society for Radiation Oncology (ASTRO) annual meeting, being held Sept. 15-18 in Chicago, Varian...
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...
Imaging Biometrics and Medical College of Wisconsin Awarded NIH Grant
News | Neuro Imaging | September 09, 2019
Imaging Biometrics LLC (IB), in collaboration with the Medical College of Wisconsin (MCW), has received a $2.75 million...
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.
Philips and Fujifilm booths at SIIM 2019.

Philips and Fujifilm booths at SIIM 2019.

Feature | SIIM | September 06, 2019 | By Greg Freiherr
Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Inform
Sudhen Desai, M.D.

Sudhen Desai, M.D.

Feature | Pediatric Imaging | September 04, 2019 | By Jeff Zagoudis
Burnout has become a popular buzzword in today’s business world, meant to describe prolonged periods of stress in the
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...
New Radiomics Model Uses Immunohistochemistry to Predict Thyroid Nodules

Workflow of radiomics analysis for IHC indicators. Yellow lines denote area of analysis; red lines denote ROI for radiomic features extraction. X = original image, L = low-pass filter, H = high-pass filter. Image courtesy of Jiabing Gu, et al.

News | Artificial Intelligence | September 03, 2019
Researchers have validated a first-of-its-kind machine learning–based model to evaluate immunohistochemical (IHC)...
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...