MammoScreen 2.0

K211541

Therapixel · cleared 2021-11-26 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“MammoScreen is a software-only device.”
Algorithmdeep learning
source quote (p.8)
“The system includes 'deep learning' modules for recognition of suspicious calcifications and soft tissue lesions.”
Adaptive (vs locked)No
source quote (p.8)
“These modules are trained with very large databases of biopsy-proven examples of breast cancer and normal tissue.”
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Reader study (MRMC)

n=240 cases

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

Reported performance (6 observations)

aurocas written: “auc”0.84
source quote (p.7)
“The performance of the standalone MammoScreen on DBT (AUC = 0.84) was found to be superior to the average performance of unaided radiologists (AUC = 0.79).”
aurocas written: “Aided Radiologist Performance (FFDM) AUC”0.8
source quote (p.7)
“The performance of radiologists taking part in the clinical study was improved when using MammoScreen 2.0, with the average AUC going from 0.77 to 0.80.”
aurocas written: “Standalone MammoScreen Performance (FFDM) AUC”0.79
source quote (p.7)
“The performance of the standalone MammoScreen on FFDM (AUC = 0.79) was found to be non-inferior to the average performance of unaided radiologists (AUC = 0.77).”
aurocas written: “Unaided Radiologist Performance (FFDM) AUC”0.77
source quote (p.7)
“The performance of the standalone MammoScreen on FFDM (AUC = 0.79) was found to be non-inferior to the average performance of unaided radiologists (AUC = 0.77).”
aurocas written: “Aided Radiologist Performance (DBT) AUC”0.83
source quote (p.7)
“The performance of radiologists taking part in the clinical study was improved when using MammoScreen 2.0, with the average AUC going from 0.79 to 0.83.”
aurocas written: “Unaided Radiologist Performance (DBT) AUC”0.79
source quote (p.7)
“The performance of the standalone MammoScreen on DBT (AUC = 0.84) was found to be superior to the average performance of unaided radiologists (AUC = 0.79).”

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
4
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

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K240301 (decision 2024-08-01) from Therapixel for a matching device line ("MammoScreen® (3)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K240301

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/K211541