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SmartMammo™

Enhanced efficiency and accuracy in breast cancer detection

SmartMammoTM is an AI-powered solution designed for seamless integration into existing breast cancer diagnostic workflows, enhancing diagnostic accuracy and workflow efficiency.

Powered by the cloud-native DeepHealth OS, it is effortlessly deployable to ensure smooth interoperability with current systems and technologies, transforming the diagnostic process into a streamlined experience.

Detect more Cancers more Accurately

AI-powered detection tools provide automatic lesion localization, degree of suspicion, and tissue density scores. The solution raises generalists’ detection rates to specialists’ levels, expanding access to expert-level mammography.

Learn about SmartMammo’s™ capabilities from Dr. Jacqueline Holt, Medical Director of Women’s Imaging at Delaware Imaging Network, RadNet.

“As a Breast Radiologist and Medical Director for a very large radiology group, I can say with absolute confidence that SmartMammoTM has been a game changer for our practice. My personal experience is that it has made me considerably more efficient due to decreased lag between loading patient studies, in particular as it relates to breast tomography where I can instantly review the current study along with multiple prior exams.” Jason R. Sinner M.D. , Medical Director, RadNet/BRMG

More cancers detected early, with fewer recalls​

10%

More cancers detected1

Specialist-level​

Performance for​
all generalists radiologists2

45.8%

Fewer​ missed cancers1

Benefits across​ diverse populations​

6.6%

More cancers detected in women with dense breasts2

10.8%

More cancers detected in Black/African American women2

Increased productivity and operational efficiency​

Less than 2 seconds

To load DBT studies​ and priors

AI-powered workflow

With smart alert to prioritize exam review and intelligent worklist sorting

Proven success

DeepHealth is powering RadNet’s EBCD breast screening program​

The Enhanced Breast Cancer Detection (EBCD) program is a breakthrough in detection. ​

It is powered by DeepHealth solutions that use artificial intelligence to help radiologists detect even subtle lesions.

Combined with exceptionally high-quality mammography systems, the EBCD program optimizes breast cancer screening, giving women greater confidence in their results.

Latest updates

  • News

    GE HealthCare and RadNet Forge Collaboration to Transform Imaging Systems and Accelerate the Adoption of Artificial Intelligence (AI) with SmartTechnology™

    November 11, 2024: Today, GE HealthCare (Nasdaq: GEHC) and DeepHealth, Inc., a global leader in AI-powered health informatics and a wholly-owned subsidiary of RadNet, Inc. (Nasdaq: RDNT), announced a strategic collaboration to further the innovation, commercialization, and adoption of AI in imaging.

  • News

    AI is transforming cancer detection – what’s next?

    October 29, 2024 - Artificial intelligence (AI) is transforming the fight against cancer by enabling faster and more accurate cancer detection. DeepHealth, a global leader in AI-powered health informatics, is pioneering new ways to advance cancer screening and broader imaging-based care, harnessing AI in an integrated, end-to-end approach to radically improve healthcare delivery.

  • News

    Deephealth Expands Its AI-Powered Cancer Diagnostic and Screening Portfolio with the Acquisition of Kheiron

    October 22, 2024 - DeepHealth has acquired Kheiron Medical Technologies Limited, a UK-based AI cancer diagnostic company focused on developing deep learning solutions to support radiologists improve breast cancer detection. Kheiron’s Mia®​​​​ (Mammography Intelligent Assessment) suite of AI solutions for breast cancer screening is a strong fit with DeepHealth’s portfolio roadmap aimed at large-scale diagnostic and screening programs leveraging AI-powered health informatics solutions.

References:
1. Saige-DX 510k pivotal study​
2 Kim et al. Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists. Radiol Artif Intell. 2024. doi: 10.1148/ryai.230137