Transpara 1.7.0

K210404

ScreenPoint Medical B.V. · cleared 2021-06-02 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Transpara® is a software only application designed to be used by physicians to improve interpretation of digital mammography and digital breast tomosynthesis.”
AlgorithmDeep learning algorithms trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
source quote (p.6)
“'Deep learning' algorithms are applied to FFDM images and DBT slices for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries). Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Standalone

n=9,122 cases

endpoints: sensitivity; false positive rate; AUC

standards: IEC 62366-1 Edition 1.1 2020-06, IEC 62366-1 Edition 1.0 2015-02 [Including CORRIGENDUM 1 (2016)], ISO 14155 Third edition 2020-07, ISO 14155 Second edition 2011-02-01, ISO 14971:2019, IEC 62304:2015, ISO 15223-1 Third Edition 2016-11-01, DEN180005

Reported performance (7 observations)

sensitivity0.947CI 91.7-96.7
source quote (p.10)
“For 2D, the sensitivity for calcifications is 94.7% (95% CI: 91.7-96.7) at a false positive rate of 0.11 FP/image.”
aurocas written: “auc”0.949
source quote (p.10)
“For 2D, AUC of the device is 0.949, which is higher is non-inferior in comparison to the AUC of 0.929 of the predicate device.”
sensitivityas written: “Sensitivity (2D soft tissue lesions)”0.802CI 76.8-83.2
source quote (p.10)
“The sensitivity for soft tissue lesions is 80.2% (95% CI: 76.8-83.2) at a false positive rate of 0.02 FP/image and 92.6% (95% CI: 90.2-94.6) at a false positive rate of 0.17 FP/image.”
sensitivityas written: “Sensitivity (2D soft tissue lesions, another FPR)”0.926CI 90.2-94.6
source quote (p.10)
“The sensitivity for soft tissue lesions is 80.2% (95% CI: 76.8-83.2) at a false positive rate of 0.02 FP/image and 92.6% (95% CI: 90.2-94.6) at a false positive rate of 0.17 FP/image.”
sensitivityas written: “Sensitivity (DBT)”0.913CI 88.1-93.6
source quote (p.10)
“For DBT, sensitivity is 91.3% (95% CI: 88.1-93.6) at a false positive rate of 0.3 FP/volume.”
aurocas written: “AUC (DBT)”0.931
source quote (p.10)
“For DBT, AUC of the device is 0.931, which is higher is non-inferior in comparison to the AUC of 0.917 of the predicate device.”
aurocas written: “AUC (Fujifilm)”0.952
source quote (p.10)
“AUC performance for Fujifilm was 0.952, which is higher is non-inferior in comparison to the AUC of 0.917 of the predicate device.”

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) K241831 (decision 2024-11-25) from ScreenPoint Medical B.V. for a matching device line ("Transpara (2.1.0)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K241831

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K232096 (decision 2023-12-11) from Screenpoint Medical B.V. for a matching device line ("Transpara Density 1.0.0") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K232096

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K221347 (decision 2022-08-03) from ScreenPoint Medical B.V. for a matching device line ("Transpara 1.7.2") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K221347

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/K210404