A new DeepHealth Breast Suite demonstrated the transformative potential of AI to detect breast cancer in screenings, with increased radiology accuracy in detecting early-stage malignancies and reducing false positives’ rates resulting in invasive follow-up procedures.
The Breast Suite platform leverages imaging tools and adds iCAD technologies – a medical technology company specializing in AI tools that enhance breast cancer detection – to provide radiologists with smarter, faster, reliable ways.
The Breast Suite delivers personalized, AI-driven breast care that combines detection, risk prediction, and workflow tools.
AI to Detect Breast Cancer
The DeepHealth Breast Suite brings multiple AI-driven tools in one environment, including cancer detection, automated density assessment, risk modelling, and an upcoming module for breast arterial calcification analysis.
Its ProFound Pro detection engine identifies potential early cancers, by comparing current images with prior scans, highlighting suspicious areas and scoring their level of concern for enhanced breast cancer detection health screening.
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Automatic density evaluation is possible for both 2D and 3D mammograms, while the AI-based risk model calculates the likelihood of a patient developing breast cancer in a year or two and is said to be twice as accurate as the traditional approach using questionnaires.
This solution aligns with findings from the recent review of PMC breast cancer imaging AI trends review. The module to detect arterial calcification, which is still in development, might also allow clinicians to evaluate cardiovascular disease risk using already existing mammogram data.
“The launch of Breast Suite marks a pivotal step toward a new AI to detect breast cancer standard of care in breast cancer screening and diagnostic pathways,” said Kees Wesdorp, President and CEO of DeepHealth.
“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,” Wesdorp added.
Streamlining Clinical Workflow
Breast Suite includes cloud-based workflow tools for managing increasing imaging volumes on behalf of the radiologist. A cloud-first viewer supports multiple imaging in one platform, such as mammography, MRI, and ultra-sound, which can be remotely accessed.
Integrated mammography reporting software promotes consistency in documentation, while key features include a prioritized worklist, rapid alerts for high-suspicion cases, and Safeguard Review, providing a second read to reduce false negatives.
Intelligent reporting automatically includes the findings on density and supports mammography artificial intelligence to ensure enhanced efficiency and accuracy. Running on DeepHealth’s operating system, the platform integrates with existing systems and receives continuous updates to meet evolving clinical needs, hence making AI mammogram reading seamless for radiologists.
Large-scale studies have validated the platform’s impact. Research published in Nature Health demonstrated that using DeepHealth tools increased the detection of breast cancer by 21%, particularly in patients with dense breasts and from diverse backgrounds.
Another study, in Science Translational Medicine, showed that when supplemental screening was focused on the highest-risk 10% of women, 44% of cancers could be detected earlier compared with 20% in traditional methods, an indication that AI detects cancer better than doctors.
By bringing together advanced AI mammograms detection, risk modeling, and workflow tools, DeepHealth’s Breast Suite offers a flexible, modular solution for health systems, enabling a shift toward more accurate, efficient, and risk-based breast cancer screening.
Health systems can adopt components individually or deploy the full suite as an integrated model, demonstrating how artificial intelligence in the treatment of breast cancer is reshaping early detection strategies. As these volumes of imaging rise, the platform also encourages radiologists to ask: Are mammograms accurate through AI? Reflecting a growing trust in machine assisted breast cancer care.
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