MammoScreen BD

K243685

Therapixel · cleared 2025-08-22 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“MammoScreen BD is a software-only device (SaMD) using artificial intelligence to assist radiologists in the interpretation of mammograms.”
Algorithmmachine-learning neural architectures, deep learning modules
source quote (p.8)
“MammoScreen BD is powered by machine-learning neural architectures. The system includes 'deep learning' modules for the assessment of the breast tissue composition.”
Adaptive (vs locked)Yes
source quote (p.12)
“MammoScreen BD is powered by machine-learning neural architectures. Therapixel will make future algorithm improvements under a PCCP. The plan describes the future modifications, assesses their impact, and a modification protocol details how data management, re-training, performance evaluation and update procedures will be handled.”
PCCPYes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Standalone

n=2,145 other · 3 site(s)

endpoints: Superiority in standalone performance for density assignment of MammoScreen BD compared to a pre-determined reference value (Kappareference = 0.85).; Quadratically weighted Cohen's kappa between the density assessment of MammoScreen BD and the established ground truth.

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 (11 observations)

sensitivity0.864
source quote (p.10)
“Predicted TPX-BD non-dense dense non-dense 100% (249) 0% (1) dense 14% (34) 86% (216)”
specificity0.996
source quote (p.10)
“Predicted TPX-BD non-dense dense non-dense 100% (249) 0% (1) dense 14% (34) 86% (216)”
accuracyas written: “Accuracy (Hologic Envision, binary task)”0.93CI [90.80, 95.20]
source quote (p.10)
“Accuracy = 93.00 with CI = [90.80, 95.20]”
agreement_kappaas written: “Cohen's Kappa (quadratic) (Hologic Envision, binary task)”0.86CI [81.36, 90.25]
source quote (p.10)
“Cohen's Kappa (quadratic) = 86.00 with CI = [81.36, 90.25]”
agreement_kappaas written: “Cohen's Kappa (linear) (Hologic Envision, binary task)”0.86CI [81.36, 90.25]
source quote (p.10)
“Cohen's Kappa (linear) = 86.00 with CI = [81.36, 90.25]”
accuracyas written: “Accuracy (Hologic Envision, four-class task)”0.854CI [82.00, 88.40]
source quote (p.10)
“Accuracy = 85.40 with CI = [82.00, 88.40]”
agreement_kappaas written: “Cohen's Kappa (quadratic) (Hologic Envision, four-class task)”0.8954CI [86.88, 91.69]
source quote (p.10)
“Cohen's Kappa (quadratic) = 89.54 with CI = [86.88, 91.69]”
agreement_kappaas written: “Cohen's Kappa (linear) (Hologic Envision, four-class task)”0.8375CI [79.85, 87.00]
source quote (p.10)
“Cohen's Kappa (linear) = 83.75 with CI = [79.85, 87.00]”
agreement_kappaas written: “Kappa quadratic (Hologic)”0.8903CI [95% CI: 87.43 – 90.56]
source quote (p.11)
“Kappa quadratic = 89.03 [95% CI: 87.43 – 90.56]”
agreement_kappaas written: “Kappa quadratic (Hologic Envision)”0.8954CI [95% CI: 86.88–91.69]
source quote (p.11)
“Kappa quadratic = 89.54 [95% CI: 86.88–91.69]”
agreement_kappaas written: “Kappa quadratic (GE)”0.9319CI [95% CI: 90.50–94.92]
source quote (p.11)
“Kappa quadratic = 93.19 [95% CI: 90.50–94.92]”

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
3
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

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/K243685