Saige Breast
Be more confident in your reading decisions.
It is difficult to find all breast cancers before they evolve into later stage cancers. Looking for that needle in the haystack day in and day out can be exhausting and mentally taxing.
The Saige Breast solution is AI-powered clinical imaging support that helps you read faster and more confidently, knowing the power of AI is there to help make the right call.
Let FDA-cleared Saige AI help improve your performance, enhance your productivity, and assist your workflow.
Explore the capabilities of Saige Breast
Capabilities
Saige Dx
Diagnostic Aid
Detect cancers. Breast cancer screening saves lives, but radiologists are overloaded.
Saige Dx Breast Diagnostic Aid is AI-powered clinical imaging support that helps you find cancer more efficiently and with greater accuracy. By drawing the radiologist’s attention quickly to suspicious lesions, the AI helps to potentially decrease recalls. It provides powerful diagnostic assistance.
Saige Dx is designed with the radiologist in mind. It produces intuitive and actionable results that fit efficiently in the radiologist’s workflow. Saige Dx assigns a Suspicion Level to each detected finding and to the entire case. The Suspicion Level indicates the strength of suspicion of the presence of cancer, ranging from Minimal (MIN), to Low (LO), Intermediate (INT), and High (HI).
Clinical features
Finding suspicion level
The Finding Suspicion Level represents the strength of suspicion that a given region of interest (outlined by a bounding box) is malignant. Depending on the case, there may be no findings or there may be multiple. Each finding is assigned its own Suspicion Level.
Case suspicion level
The Case Suspicion Level indicates the strength of suspicion that the overall case contains at least one malignant finding. The Saige Dx algorithm combines information from all processed images and findings into a single Suspicion Level.
Make decisions confidently with the support of Saige Assure. Paired with Saige Dx, it provides an AI-driven workflow for additional review of the most suspicious exams to ensure top screening performance.
Capabilities
Saige Q Breast
Mammography Triage
Prioritize suspicious exams quickly and efficiently. Address the most pressing exams first while keeping up with the growing volume of mammograms in your practice.
Saige Q Breast Mammography Triage is AI-powered worklist support that adds actionable information to organize your case load.
It identifies suspicious cases automatically, flags them on your worklist, and displays preview images.
Capabilities
Saige Density
Breast Assessment
Categorize quickly and consistently. Assessing breast density consistently across all of your screening mammograms can be challenging, especially when it could lead to additional imaging for the patient.
Saige Density Breast Assessment is AI-powered assessment support for breast density. It automatically generates an initial ACR BI-RADS® 5th Edition breast density category to help radiologists evaluate fibroglandular tissue.
Saige Density Breast Assessment’s performance is consistently accurate across a wide range of races, ethnicities, ages, breast densities, FFDM and DBT modalities, screening and diagnostic exam types, and manufacturers including Hologic and GE.
Clinical features
Indicator bar.
Radiologists are able to view the outputted density category on their mammography viewing workstation.
Saige Density produces an indicator bar that displays the ranking of the exam within the specific density category. The indicator bar helps radiologists visualize where the exam falls within the outputted density category.
Density category.
The density category can be displayed as an overlay on the original images.
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Testimonials
Testimonial“Ultimately, a much more robust nodule follow-up—in half the time.”
Dr. Katharine Johnson
Consultant Radiologist at Salisbury NHS
Testimonial“It supports us in improving cancer care through more detailed reporting.”
Dr. Thomas Jongsma
Tergooi Hospital, NL
Learn more
Scientific Publication
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
William Lotter, Abdul Rahman Diab, Bryan Haslam, Jiye G. Kim, Giorgia Grisot, Eric Wu, Kevin Wu, Jorge Onieva Onieva, Jerrold L. Boxerman, Meiyun Wang, Mack Bandler, Gopal Vijayaraghavan, A. Gregory Sorensen
NATURE MEDICINE January 11, 2021
