iCAD showcased its ProFound AI™ for Digital Breast Tomosynthesis (DBT), or 3D mammography, the first and only FDA-cleared artificial intelligence (AI) solution that supports breast cancer detection in DBT.
ProFound AI helps increase cancer detection rates while also reducing false positives and reading time to enhance clinical performance. According to a recent study, ProFound AI is clinically proven to increase sensitivity by 8%, reduce false positives by 7% and slash radiologists’ reading time by 52%.
“With ProFound AI for DBT, radiologists are still in the driver’s seat – they’re just getting help with prioritizing which lesions to look at, which essentially cuts reading time in half. It’s especially helpful on challenging cases where a radiologist might need extra insight,” said Jeff Hoffmeister, MD, MSEE, vice president and medical director of iCAD.
The ProFound AI platform is a high-performing, concurrent-read workflow solution featuring the latest in deep-learning AI capabilities. Trained with one of the largest available datasets, the software rapidly and accurately analyzes each DBT image and provides radiologists with key information, such as Certainty of Finding lesion and Case Scores, which assists in clinical decision-making and prioritizing caseloads. It allows for continuously improved performance via ongoing updates.
ProFound AI for DBT runs on the industry-leading PowerLook platform with NVIDIA Graphical Processing Units (GPU). PowerLook is a ﬂexible and reliable DICOM platform that easily integrates with image modalities, mammography review workstations, PACS, and image storage systems. Leveraging the latest in GPU technology, the algorithm can rapidly process a 4-view tomosynthesis case, ensuring results are available to radiologists in the most efficient manner. Currently, ProFound AI for DBT is available for use with leading DBT systems in Europe, Canada and the United States and its adoption continues to grow globally.
iCAD anticipates it will soon launch a new short-term risk model that will help clinicians predict which patients might develop cancer in the future to determine who should receive supplemental screening.