Saige-Q

K203517

DeepHealth, Inc. · cleared 2021-04-16 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“Saige-Q is a software workflow tool designed to aid radiologists in prioritizing exams within the standard-of-care image worklist for compatible full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) screening mammograms.”
Algorithmartificial intelligence algorithm using deep neural networks
source quote (p.5)
“The preprocessed images become the input to the AI algorithm, which generates the Saige-Q code using deep neural networks that have been trained on large numbers of mammograms where cancer status is known.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Retrospective clinical

n=1,333 cases · 8 site(s)

endpoints: sensitivity; specificity; processing time

Retrospective clinical

n=1,528 cases · 6 site(s)

endpoints: sensitivity; specificity; processing time

Reported performance (14 observations)

sensitivity0.912CI [88.4%, 93.4%]
source quote (p.8)
“The primary endpoint for FFDM was successfully met with Saige-Q demonstrating a specificity at 86.9% sensitivity of 92.2% (95% CI: [90.2%, 93.8%]) and a sensitivity at 88.9% specificity of 91.2% (95%: [88.4%, 93.4%]).”
specificity0.922CI [90.2%, 93.8%]
source quote (p.8)
“The primary endpoint for FFDM was successfully met with Saige-Q demonstrating a specificity at 86.9% sensitivity of 92.2% (95% CI: [90.2%, 93.8%]) and a sensitivity at 88.9% specificity of 91.2% (95%: [88.4%, 93.4%]).”
aurocas written: “auc”0.966CI [0.957, 0.975]
source quote (p.8)
“In the FFDM study, Saige-Q achieved an overall area under the receiver operating characteristic curve (AUC) of 0.966 (95% CI: [0.957, 0.975]).”
aurocas written: “AUC (DBT)”0.985CI [0.979, 0.990]
source quote (p.8)
“In the DBT study, Saige-Q achieved an overall AUC of 0.985 (95% CI: [0.979, 0.990]) on the DBT data.”
sensitivityas written: “Sensitivity (DBT) at 89.9% Specificity”0.957CI [93.6%, 97.2%]
source quote (p.8)
“The primary endpoint for DBT was also successfully met with Saige-Q demonstrating a specificity at 86.9% sensitivity: 98.3% (95% CI: [97.3%, 99.0%]) and a sensitivity at 89.9% specificity of 95.7% (95% CI: [93.6%, 97.2%]).”
sensitivityas written: “Specificity (DBT) at 86.9% Sensitivity”0.983CI [97.3%, 99.0%]
source quote (p.8)
“The primary endpoint for DBT was also successfully met with Saige-Q demonstrating a specificity at 86.9% sensitivity: 98.3% (95% CI: [97.3%, 99.0%]) and a sensitivity at 89.9% specificity of 95.7% (95% CI: [93.6%, 97.2%]).”
aurocas written: “AUC (FFDM, soft tissue densities)”0.964CI [0.954, 0.974]
source quote (p.9)
“For instance, on FFDM, Saige-Q achieved an AUC of 0.964 (95% CI: [0.954, 0.974]) on soft tissue densities and an AUC of 0.973 (95% CI: [0.958, 0.988]) on calcifications.”
aurocas written: “AUC (FFDM, calcifications)”0.973CI [0.958, 0.988]
source quote (p.9)
“For instance, on FFDM, Saige-Q achieved an AUC of 0.964 (95% CI: [0.954, 0.974]) on soft tissue densities and an AUC of 0.973 (95% CI: [0.958, 0.988]) on calcifications.”
aurocas written: “AUC (DBT, soft tissue densities)”0.983CI [0.977, 0.990]
source quote (p.9)
“For DBT, Saige-Q achieved an AUC of 0.983 (95% CI: [0.977, 0.990]) on soft tissue densities and an AUC of 0.989 (95% CI: [0.983, 0.996]) on calcifications.”
aurocas written: “AUC (DBT, calcifications)”0.989CI [0.983, 0.996]
source quote (p.9)
“For DBT, Saige-Q achieved an AUC of 0.983 (95% CI: [0.977, 0.990]) on soft tissue densities and an AUC of 0.989 (95% CI: [0.983, 0.996]) on calcifications.”
aurocas written: “AUC (FFDM, dense breasts)”0.959CI [0.945, 0.973]
source quote (p.9)
“For breast density, Saige-Q achieved an AUC of 0.959 (95% CI: [0.945, 0.973]) on dense breasts and an AUC of 0.972 (95% CI: [0.961, 0.984]) on non-dense breasts for FFDM exams.”
aurocas written: “AUC (FFDM, non-dense breasts)”0.972CI [0.961, 0.984]
source quote (p.9)
“For breast density, Saige-Q achieved an AUC of 0.959 (95% CI: [0.945, 0.973]) on dense breasts and an AUC of 0.972 (95% CI: [0.961, 0.984]) on non-dense breasts for FFDM exams.”
aurocas written: “AUC (DBT, dense breasts)”0.98CI [0.971, 0.988]
source quote (p.9)
“For DBT, Saige-Q achieved an AUC of 0.980 (95% CI: [0.971, 0.988]) on dense breasts and an AUC of 0.988 (95% CI: [0.981, 0.996]) on non-dense breasts.”
aurocas written: “AUC (DBT, non-dense breasts)”0.988CI [0.981, 0.996]
source quote (p.9)
“For DBT, Saige-Q achieved an AUC of 0.980 (95% CI: [0.971, 0.988]) on dense breasts and an AUC of 0.988 (95% CI: [0.981, 0.996]) on non-dense breasts.”

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
6
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

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K243705 (decision 2024-12-19) from DeepHealth, Inc for a matching device line ("Saige-Density (2.5.0)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K243705

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K243688 (decision 2024-12-19) from DeepHealth, Inc. for a matching device line ("Saige-Dx (3.1.0)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K243688

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K241747 (decision 2024-11-18) 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:K241747

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K222275 (decision 2022-12-16) from DeepHealth, Inc. for a matching device line ("Saige-Density") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K222275

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K220105 (decision 2022-05-12) 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:K220105

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. 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).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K203517