Saige-Dx (3.1.0)

K243688

DeepHealth, Inc. · cleared 2024-12-19 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing reading time. The software takes as input a set of x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study and generates finding-level outputs for each image analyzed, as well as an aggregate case-level assessment. Saige-Dx processes both the DBT image stacks and the associated 2D images (full-field digital mammography (FFDM) and/or synthetic 2D images) in a DBT study. For each image, Saige-Dx outputs bounding boxes circumscribing any detected findings and assigns a Finding Suspicion Level to each finding, indicating the degree of suspicion that the finding is malignant. Saige-Dx uses the results of the finding-level analysis to generate a Case Suspicion Level, indicating the degree of suspicion for malignancy across the case. Saige-Dx encapsulates the finding and case-level results into a DICOM Structured Report (SR) object containing markings that can be overlaid on the original mammogram images using a viewing workstation and a DICOM Secondary Capture (SC) object containing a summary report of the Saige-Dx results. Saige-Dx is a software only device.
Algorithmartificial intelligence (AI)/machine learning algorithms that analyze mammography images to detect and characterize findings and provide information regarding the presence and location of the findings to the user.
source quote (p.6)
Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing reading time. Both the subject and predicate devices are software systems that use artificial intelligence (AI)/machine learning algorithms that analyze mammography images to detect and characterize findings and provide information regarding the presence and location of the findings to the user.
Adaptive (vs locked)No
source quote (p.7)
The design of the current version of Saige-Dx is similar to that of the predicate device. Verification and Validation testing has been completed ensuring that the differences do not affect the safety and effectiveness of the proposed subject device.
PCCPNo
Cybersecurity addressedNo

Validation studies (2)

Reader study (MRMC)

sample size not stated

standards: ISO 14971:2019 - Medical Devices - Application of Risk Management to Medical Devices (#5-125), IEC 62304:2015 – Medical Device Software – Software Life Cycles Processes (#13-79), NEMA PS3 – Digital Imaging and Communications in Medicine (DICOM) Set (#12-300), Guidance for Industry and FDA Staff: Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (May, 2005), Guidance for Industry and FDA Staff: Software as a Medical Devices (SAMD): Clinical Evaluation (December 2017)

Retrospective clinical

sample size not stated

standards: ISO 14971:2019 - Medical Devices - Application of Risk Management to Medical Devices (#5-125), IEC 62304:2015 – Medical Device Software – Software Life Cycles Processes (#13-79), NEMA PS3 – Digital Imaging and Communications in Medicine (DICOM) Set (#12-300), Guidance for Industry and FDA Staff: Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (May, 2005), Guidance for Industry and FDA Staff: Software as a Medical Devices (SAMD): Clinical Evaluation (December 2017)

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
vs code's own 3-yr baseline
1
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K251873 (decision 2025-08-11) from DeepHealth, Inc. for a matching device line ("Saige-Dx") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K251873

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K243688