MammoScreen® (3)

K240301

Therapixel · cleared 2024-08-01 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“MammoScreen is a concurrent reading medical software device using artificial intelligence to assist radiologists in the interpretation of mammograms.”
Algorithmmedical image processing and machine learning techniques; deep learning modules
source quote (p.7)
“For both devices, a choice of medical image processing and machine learning techniques are implemented. The systems includes 'deep learning' modules for the detection of suspicious findings.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedNo

Validation studies (2)

Reader study (MRMC)

n=240 cases · 3 site(s)

endpoints: Whether the performance of radiologists when using MammoScreen 3 is superior to unaided radiologist for interpretation of screening mammograms (primary objective).; Whether the performance of MammoScreen 3 standalone is superior to unaided radiologist performance.; Whether the performance of MammoScreen 3 standalone is non-inferior to aided radiologist performance.

standards: IEC 62304:2006/A1:2016- Medical device software - Software life-cycle processes, IEC 62366-1:2015+AMD1:2020- Medical devices - Application of usability engineering to medical devices.

Retrospective clinical

n=4,429 patients · 3 site(s)

endpoints: AUROC at mammogram level; Sensitivity at mammogram level; Specificity at mammogram level; Positive Percentage Agreement (PPA) in lesion type assessment; Negative Percentage Agreement (NPA) in lesion type assessment; Positive Percentage Agreement (PPA) in CC/MLO quadrant assessment; Negative Percentage Agreement (NPA) in CC/MLO quadrant assessment; Positive Percentage Agreement (PPA) in depth assessment; Negative Percentage Agreement (NPA) in depth assessment

Reported performance (3 observations)

sensitivity0.833CI [0.756 – 0.911]
source quote (p.9)
“The sensitivity and specificity values at the mammogram level of MammoScreen as a standalone system were respectively 0.833 [0.756 – 0.911] and 0.793 [0.728 – 0.858].”
specificity0.793CI [0.728 – 0.858]
source quote (p.9)
“The sensitivity and specificity values at the mammogram level of MammoScreen as a standalone system were respectively 0.833 [0.756 – 0.911] and 0.793 [0.728 – 0.858].”
aurocas written: “auc”0.883CI [0.837 – 0.929]
source quote (p.9)
“The AUC value at the mammogram level of MammoScreen as a standalone system was 0.883 [0.837 – 0.929] and was found to be superior to radiologists in unaided reading conditions (ΔAUC=+0.086 [0.048 – 0.127], p-value <0.0001) and non-inferior to radiologists in aided reading conditions (ΔAUC=-0.012 [-0.015 – -0.039], p-value <0.0001).”

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
3
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K243685 (decision 2025-08-22) from Therapixel for a matching device line ("MammoScreen BD") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K243685

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K243679 (decision 2025-07-03) from Therapixel for a matching device line ("MammoScreen® (4)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K243679

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K241561 (decision 2024-10-02) from Therapixel for a matching device line ("MammoScreen BD") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K241561

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/K240301