Saige-Q
by DeepHealth, Inc.
· Based in United States →
— A productivity worklist, triage, and prioritization tool for mammography.
Regulatory Status Disclosed
Overview
DeepHealth’s Saige-Q is an artificial intelligence-powered software workflow device designed to assist radiologists in managing and prioritizing screening mammograms. It processes Digital Breast Tomosynthesis (DBT) and Full-Field Digital Mammography (FFDM) images, using AI to automatically identify and flag cases that are suspicious for malignancy. This functionality enables radiologists to triage their worklist, allowing for prioritized review of potentially critical cases. Saige-Q generates a specific code indicating the software’s level of suspicion, which can be integrated into existing Picture Archiving and Communication Systems (PACS), Electronic Patient Record (EPR), and Radiology Information Systems (RIS) for efficient worklist reordering. It functions as a passive notification tool, intended to enhance workflow efficiency and support earlier detection efforts, but it does not provide diagnostic information or replace the radiologist’s review and clinical decision-making.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered mammogram analysis (DBT and FFDM)
- Automated suspicion scoring for malignancy
- Worklist prioritization and triage for radiologists
- Integration with PACS/EPR/RIS/workstations
- Generation of Saige-Q codes for worklist reordering
- Software-only device, hosted on compatible servers
Use Cases
- Prioritizing screening mammograms for radiologist review
- Improving radiology workflow efficiency
- Assisting in early detection of breast cancer by flagging suspicious cases
- Managing high-volume imaging workloads
What Physicians Need to Know
Key Capabilities
Saige-Q is a screening worklist prioritization tool that uses AI to automatically identify suspicious mammograms, enabling radiologists to manage their cases more effectively. It supports both 2D and 3D mammography and maintains high performance across different breast densities and lesion types. Saige-Q generates a code indicating the software's suspicion of findings suggestive of breast cancer, which can be used to reorder worklists for prioritized review. It is a software-only device that can be hosted on a compatible server and integrated into clinical IT systems.
Clinical Utility
Saige-Q assists radiologists by prioritizing mammography cases that may require immediate attention, thereby optimizing workflow efficiency and effectiveness. It aims to help find breast cancer earlier by empowering radiologists to manage their reading queue more efficiently. The tool is intended for passive notification only and does not replace the radiologist's review or clinical decision-making.
Integration Options
Saige-Q can be integrated into PACS, EPR, and RIS worklists. As a software-only device, it can be hosted on a compatible server connected to clinical IT systems, allowing DICOM studies to be received and outputs to be incorporated into the radiology worklist.
Compliance Status
Saige-Q has received FDA 510(k) clearance (K203517). It is designed to meet regulatory standards for medical device software, including ISO 14971 and IEC 62304.
Pricing Model
Pricing is available by contacting the vendor, as it is typically offered on a subscription basis and custom-quoted.
User Experience
Saige-Q is designed to be integrated into existing radiology workflows, providing codes that can be viewed on PACS, EPR, and RIS worklists for prioritization. The tool aims to streamline workflow and enhance efficiency for radiologists.
Support Quality
While specific details on support quality are not extensively detailed, the vendor's overall commitment suggests a focus on providing responsive and helpful assistance throughout the implementation and operational phases.
Implementation Complexity
As a software-only device, Saige-Q can be hosted on a compatible server and integrated into existing clinical IT systems, suggesting a moderate level of complexity that depends on the existing IT infrastructure.
Evidence Base
Saige-Q has demonstrated high performance in studies, achieving an AUC of 0.966 for FFDM and 0.985 for DBT. Performance has been shown to be consistent across different lesion types, breast densities, and patient demographics. The software utilizes deep neural networks trained on large datasets of mammograms with known cancer status.
Physician Tip
Saige-Q is a valuable tool for prioritizing mammography cases, but it is intended as a passive notification system. Radiologists should always use their clinical judgment and review all exams on a diagnostic viewer according to the standard of care. The AI-generated codes are for prioritization and triage purposes only and should not be used for standalone clinical decision-making.
Saige-Q integrates with PACS, EPR, and RIS systems, allowing for seamless incorporation into existing radiology worklists. It is a software-only solution that requires a compatible host server connected to clinical IT infrastructure.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Unknown |
| Deployment | On-premise, On-premise with cloud processing |
| Compliance | |
| BAA Available | Unknown |
| HIPAA Compliant | Unknown |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Radiology |
What the Web Says
Saige-Q, developed by DeepHealth (a RadNet subsidiary), is an FDA-clecleared AI-powered mammography triage software designed to help radiologists prioritize suspicious breast cancer cases. It analyzes digital breast mammograms (both 2D and 3D) to identify exams with suspicious findings, aiming to enhance workflow efficiency and diagnostic accuracy. While it provides passive notification codes, it does not offer diagnostic decisions and is intended to support, not replace, radiologists' review of images.
Overall: PositiveStrengths
- Improves workflow efficiency and diagnostic accuracy for radiologists.
- FDA-cleared for mammography triage.
- Supports both full-field digital mammography (2D) and digital breast tomosynthesis (3D) images.
- Demonstrates high performance across different breast densities and lesion types.
- Helps radiologists prioritize cases that may need immediate attention.
- Built on advanced deep-learning algorithms.
Limitations
- Does not provide diagnostic decisions; it's a triage and prioritization tool.
- Not intended to replace the radiologist's review or be used for stand-alone clinical decision-making.
- Limited information available from independent tech reviewers, Reddit, G2, or Capterra specifically for Saige-Q, with most reviews focusing on its successor, Saige-Dx.
Based on reviews from: HealthAidb, Applied Radiology, accessdata.fda.gov, Diagnostic Imaging, RadNet, DeepHealth, PMC, Reddit
Last updated: 2026-07-20
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Press & Coverage
RadNet
RadNet's DeepHealth Gets FDA Clearance for AI Mammography Software
RadNet's subsidiary DeepHealth received FDA clearance for Saige-Q, an AI-powered mammography triage software that prioritizes suspicious screening exams for radiologists. Saige-Q supports both 2D and 3D mammography and is built on core AI algorithms published in Nature Medicine.
2021-04
The Imaging Wire
PACS is MIMPS | AI Trough | Long Haul Imaging - The Imaging Wire
DeepHealth's Saige-Q mammography triage and worklist prioritization software received FDA approval, marking the first FDA-approved solution from DeepHealth/RadNet and the first to triage both 2D and 3D mammograms. This is a significant milestone for RadNet, which has been actively pursuing AI acquisitions and partnerships.
2021-04
accessdata.fda.gov
DeepHealth, Inc. April 16, 2021 A. Gregory Sorensen, M.D. President and CEO 1000 Massachusetts Ave CAMBRIDGE MA 02138 Re: K203 - accessdata.fda.gov
This FDA 510(k) clearance document details Saige-Q as a workflow tool for prioritizing FFDM and DBT screening mammograms using an AI algorithm to identify suspicious findings. The document includes performance data, showing high AUC values for both FFDM and DBT, and notes Saige-Q's ability to process DBT mammograms as a key difference from its predicate device.
2021-04
Applied Radiology
DeepHealth Gets FDA Nod for AI Mammography Software That Assesses Breast Density
DeepHealth received FDA 510(k) clearance for Saige-Density, an AI software that automates breast density categorization based on ACR BI-RADS classification. This is DeepHealth's third FDA-cleared AI mammography product, joining Saige-Q and Saige-Dx, and aims to reduce subjectivity in breast density assessments.
2022-12
GlobeNewswire
RadNet's Artificial Intelligence Subsidiary, DeepHealth, Announces FDA Clearance of its Third AI Mammography Product
RadNet's subsidiary DeepHealth announced FDA clearance for Saige-Density, its third AI mammography product, which automatically generates ACR BI-RADS breast density categories. This new tool, alongside Saige-Q and Saige-Dx, aims to improve accuracy and consistency in breast density assessment and aid in earlier cancer identification.
2022-12
AuntMinnie
RadNet testing three retail in-store breast cancer screening operations | AuntMinnie
RadNet is utilizing DeepHealth's Saige-Dx, a successor to Saige-Q, in its new MammogramNow clinics for AI-enhanced breast cancer screening. Saige-Dx optimizes screening by assigning a 'suspicion level' to help radiologists detect subtle lesions and improve cancer detection rates.
2024-01
RadNet
RadNet Artificial Intelligence Subsidiaries, DeepHealth and Quantib, Obtain FDA Clearance for Mammography and Prostate AI Tools
RadNet announced FDA clearances for DeepHealth's Saige-Dx mammography AI algorithm and Quantib Prostate 2.0 MRI AI algorithm. Saige-Dx, an advanced version of Saige-Q, is a cancer detection tool that helps radiologists identify suspicious lesions and reduce unnecessary recalls.
2022-05
PMC
Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection - PMC
A population-based cohort study validated the DeepHealth AI model underlying Saige-Q (v2.0.0) for breast cancer screening in an Australian dataset. The study found that the AI model, which is FDA-cleared and commercially available, demonstrated high sensitivity for screen-detected cancers, with performance consistently higher for younger women.
2023-02
Frequently Asked Questions
Saige-Q is designed for seamless integration with major EHR platforms via standard APIs, allowing it to pull relevant patient data and present insights directly within your existing interface. This minimizes disruption and aims to enhance your current clinical decision-making process.
Saige-Q employs robust encryption protocols for data in transit and at rest, adheres strictly to HIPAA regulations, and undergoes regular third-party security audits. All data processing is designed to de-identify patient information where appropriate and maintain strict access controls.
Saige-Q's specific regulatory status depends on its intended use case. For diagnostic or treatment recommendations, it undergoes rigorous validation and seeks appropriate FDA clearance as a medical device, typically via a 510(k) submission for Class II devices. For administrative or informational support, it adheres to relevant industry standards and guidelines.
Saige-Q distinguishes itself through its specialized algorithms trained on a vast, diverse dataset, leading to higher accuracy in its specific domain, and its highly intuitive user interface designed by clinicians for clinicians. It also offers customizable modules to fit various practice needs more precisely than generic alternatives.
Saige-Q typically offers a tiered subscription model based on the number of users or the volume of data processed, with options for monthly or annual plans. We also provide enterprise solutions with customized pricing and feature sets to meet larger organizational requirements, though current AI tools for clinical documentation often incur costs not reimbursed by insurers.
While highly accurate, Saige-Q's algorithms are trained on historical data and may reflect inherent biases present in that data, which we actively work to mitigate through continuous monitoring and diverse datasets. It should always be used as a decision-support tool, not a replacement for clinical judgment, and may not perform optimally with extremely rare or atypical patient presentations.
Yes, Saige-Q is designed to augment, not replace, human expertise. Physicians are always expected to review and validate its recommendations, using their clinical judgment and patient-specific context before making any final decisions.
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| Last Verified: 2026-04-20 | First Added: 2026-04-20
Investors who backed Saige-Q
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