The current version of the algorithm was trained using thousands of images. "The use of deep learning has enabled us to achieve a high detection rate of 92.3 percent." Deep learning is 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.
ICAD FDA APPROVAL SOFTWARE
"This innovative software in PowerLook Tomo Detection is built on the latest deep learning technology to significantly improve detection rates," said Jeff Hoffmeister, MD, Vice President and Medical Director at iCAD. 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. 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. The study included 20 radiologists reading 240 tomosynthesis cases both with and without the PowerLook Tomo Detection technology. In a U.S., clinical study conducted from October 2015 to January 2016, radiologists were able to significantly reduce reading time when reading 3D tomosynthesis exams with PowerLook Tomo Detection. "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." 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, MD, Medical Director and diagnostic radiologist, Intrinsic Imaging, LLC, who took part in the U.S. "iCAD has taken a refreshing new approach to computer aided detection. The suspicious areas are naturally blended onto a 2D synthetic image to provide radiologists with a single enhanced image that is used to more efficiently navigate the large tomosynthesis data set. PowerLook Tomo Detection improves radiologists' efficiency by automatically analyzing each tomosynthesis plane and identifying suspicious areas.
with the potential to greatly improve clinical workflow and patient care in the years ahead."ĢD digital mammography typically produces four images per exam while digital breast 3D tomosynthesis can produce hundreds of images, significantly increasing exam interpretation time for radiologists. "FDA approval of PowerLook Tomo Detection is a significant milestone for the Company and we are positioned to rapidly advance our commercial plan for this innovative technology in the U.S. and abroad, iCAD remains committed to delivering innovative solutions that help radiologists be more efficient and confident when reading tomosynthesis exams," said Ken Ferry, CEO of iCAD. "As the use of 3D mammography continues to grow in the U.S. PowerLook Tomo Detection is a first-of-its-kind, concurrent-read computer aided detection solution for digital breast 3D tomosynthesis and is the latest innovation available on the PowerLook® Breast Health Solutions platform. ICAD (Nasdaq: ICAD) announced that PowerLook® Tomo Detection received Premarket Approval (PMA) from the U.S. NET Framework Version:9 ASP.NET Version.Get inside Wall Street with StreetInsider Premium. Page.ProcessRequestMain(Boolean includeStagesBeforeAsyncPoint, Boolean includeStagesAfterAsyncPoint) +1533 _Load(Object sender, EventArgs e) in D:\SaffronMedia\TFS Applications\Pharmabiz\Pharmabiz\Pharmabiz\:140 Information regarding the origin and location of the exception can be identified using the exception stack trace below. Please review the stack trace for more information about the error and where it originated in the code.Įxception Details: System.FormatException: Input string was not in a correct format.Īn unhandled exception was generated during the execution of the current web request. Description: An unhandled exception occurred during the execution of the current web request. Input string was not in a correct format.
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