Transpara (2.1.0)

K241831

ScreenPoint Medical B.V. · cleared 2024-11-25 · 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 full-field digital mammography (FFMD) and digital breast tomosynthesis (DBT).”
AlgorithmDeep learning algorithms are applied to images 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.
source quote (p.6)
“Deep learning algorithms are applied to images 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 addressedYes
source quote (p.9)
“Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions”

Validation studies (2)

Retrospective clinical

n=10,207 cases · 7 site(s)

endpoints: Exam based sensitivity for cancer detection; False positive rates

standards: IEC 62366-1, ISO 20417, ISO 14971, IEC 62304, IEC 82304-1, ISO 15223-1, FDA-1997-D-0029, FDA-2011-D-0652, FDA-2015-D-5105, FDA-2011-D-0469, FDA-2015-D-4852, FDA-2014-D-0456, FDA-2018-D-1329, FDA-2016-D-1853, FDA-2009-D-0593, FDA-2009-D-0503, FDA-2019-D-1470, FDA 2021-D-1158, FDA-2021-D-0775, FDA-2019-D-3598, FDA-2023-D-1030, FDA-2021-D-0872

Retrospective clinical

n=5,724 cases

endpoints: Exam based sensitivity for cancer detection; False positive rates

Reported performance (17 observations)

sensitivity0.974CI 96.3 - 98.5
source quote (p.10)
“FFDM 97.4% (96.3 - 98.5)”
specificity0.7
source quote (p.10)
“Sensitivity for Sensitive Mode (70% specificity)”
aurocas written: “auc”0.96CI 0.953 - 0.966
source quote (p.10)
“0.960 (0.953 - 0.966)”
sensitivityas written: “FFDM Sensitivity for Specific Mode (80% specificity)”0.952CI 93.7 - 96.7
source quote (p.10)
“95.2% (93.7 - 96.7)”
sensitivityas written: “FFDM Sensitivity for Elevated Risk (97% specificity)”0.808CI 78.0 - 83.6
source quote (p.10)
“80.8% (78.0-83.6)”
aurocas written: “DBT Exam-based AUC”0.955CI 0.947 - 0.963
source quote (p.10)
“0.955 (0.947 - 0.963)”
sensitivityas written: “DBT Sensitivity for Sensitive Mode (70% specificity)”0.969CI 95.5-98.3
source quote (p.10)
“96.9% (95.5-98.3)”
sensitivityas written: “DBT Sensitivity for Specific Mode (80% specificity)”0.951CI 93.3- 96.8
source quote (p.10)
“95.1% (93.3- 96.8)”
sensitivityas written: “DBT Sensitivity for Elevated Risk (97% specificity)”0.784CI 75.1- 81.7
source quote (p.10)
“78.4% (75.1- 81.7)”
aurocas written: “FFDM with TA Exam-based AUC”0.958CI 0.946 - 0.969
source quote (p.12)
“0.958 (0.946 - 0.969)”
sensitivityas written: “FFDM with TA Sensitivity for Sensitive Mode (70% specificity)”0.957CI 93.7 - 97.6
source quote (p.12)
“95.7% (93.7 - 97.6)”
sensitivityas written: “FFDM with TA Sensitivity for Specific Mode (80% specificity)”0.954CI 93.4 - 97.4
source quote (p.12)
“95.4% (93.4 - 97.4)”
sensitivityas written: “FFDM with TA Sensitivity for Elevated Risk (97% specificity)”0.827CI 79.1 - 86.4
source quote (p.12)
“82.7% (79.1 - 86.4)”
aurocas written: “DBT with TA Exam-based AUC”0.941CI 0.921 - 0.958
source quote (p.12)
“0.941 (0.921 - 0.958)”
sensitivityas written: “DBT with TA Sensitivity for Sensitive Mode (70% specificity)”0.946CI 91.2 - 98.0
source quote (p.12)
“94.6% (91.2 - 98.0)”
sensitivityas written: “DBT with TA Sensitivity for Specific Mode (80% specificity)”0.91CI 86.7 - 95.4
source quote (p.12)
“91.0% (86.7 - 95.4)”
sensitivityas written: “DBT with TA Sensitivity for Elevated Risk (97% specificity)”0.749CI 68.3 - 81.4
source quote (p.12)
“74.9% (68.3 - 81.4)”

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
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/K241831