Can DeepHealth AI Suite Transform Breast Cancer Screening?

DeepHealth, a RadNet company, has launched the DeepHealth Breast Suite – a modular AI platform designed to enhance breast cancer detection, risk stratification and clinical workflow. 

The suite builds on the company’s existing imaging technologies and integrates tools from iCAD to create a unified set of applications that support more than 10 million mammograms each year.

Kees Wesdorp, President and CEO of DeepHealth, says: “The launch of Breast Suite marks a pivotal step toward a new, AI-powered standard of care in breast cancer screening and diagnostic pathways. 

“By embedding detection and risk intelligence with workflow tools, we give radiologists more capabilities to detect cancers earlier, with more confidence and to elevate patient care.”

Integrated AI for early detection

DeepHealth Breast Suite brings together AI-powered cancer detection, automated density assessment, risk modelling and in-development breast arterial calcification analysis within one environment. 

Its ProFound Pro detection engine supports earlier disease identification by using prior data, automatic localisation of regions of interest and degree of suspicion scoring.

The suite also includes automated density assessment for 2D and 3D mammograms and an AI-driven risk model that estimates the likelihood of developing breast cancer in one to two years. 

DeepHealth states that this model delivers twice the accuracy of traditional questionnaire-based tools.

In addition, the company is developing a module that identifies breast arterial calcifications to assist clinicians in assessing cardiovascular disease risk using existing mammograms.

These features have undergone large-scale real-world validation.

A Nature Health study analysed mammograms from more than 579,000 women across more than 100 community imaging sites. 

DeepHealth technology enabled a 21% increase in breast cancer detection rate, with higher detection gains in dense-breast and diverse patient populations.

The study also found that the tools helped generalist radiologists reach performance levels associated with more specialised readers.

A separate Science Translational Medicine study evaluated DeepHealth’s risk assessment model in 154,000 women in Europe.

Researchers found that if additional screening had been offered only to the highest risk 10%, 44% of cancers could have been detected earlier. This compares with 20% using the Tyrer-Cuzick model.

Workflow tools built for modern imaging teams

Alongside detection and risk capabilities, Breast Suite includes cloud-based workflow tools designed to support radiologists handling growing imaging volumes. 

The cloud-first viewer supports multimodality images, including mammography, MRI and ultrasound, and can be accessed remotely.

Workflow features include a prioritised worklist, rapid case alerts for high suspicion findings and the Safeguard Review workflow, which provides a second review to reduce false negatives. 

Intelligent reporting allows guideline-based templates and automatic population of density findings to support consistency.

The suite runs on DeepHealth’s operating system, which integrates with customer environments and supports remote access.

The company says the platform will continue to evolve through ongoing updates in response to clinical needs.

Supporting risk-stratified screening pathways

With rising imaging volumes and growing interest in personalised screening strategies, the launch of Breast Suite aligns DeepHealth’s tools into a more streamlined AI environment.

The company highlights that the suite is already used across millions of annual mammograms and is designed to support screening programmes moving toward greater accuracy, efficiency and risk-based decision-making.

The platform’s modular approach enables health systems to adopt individual components or deploy the entire suite as an integrated model, providing flexibility for organisations with different clinical and operational requirements.