Transpara Density 1.0.0
K232096Screenpoint Medical B.V. · cleared 2023-12-11 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.3)
“Transpara Density is a software application intended for use with data from compatible digital mammography and digital breast tomosynthesis systems.”
source quote (p.3)
“Transpara Density utilises deep learning artificial intelligence algorithms to automatically determine volumetric breast density (VBD), breast volume, and an ACR BI-RADS 5th Edition breast density category to aid health care professionals in the assessment of breast tissue composition.”
source quote (p.13)
“ScreenPoint has applied a risk management process in accordance with FDA recognized standards to identify, evaluate, and mitigate all known hazards related to Transpara Density.”
Validation studies (7)
Bench
sample size not stated
standards: IEC 62366-1 Edition 1.1 2020-06, ISO 14155 Third edition 2020-07, ISO 14971 Third Edition 2019-12, IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION, IEC 82304-1: 2016, ISO 15223-1 Fourth Edition 2021-07, ISO 20417 First edition 2021-04 Corrected version 2021-12
Retrospective clinical
n=5,468 cases
endpoints: Pearson correlation between VBD computed with Transpara Density and the physics model; Pearson correlation coefficient for breast volume
Retrospective clinical
n=190 cases
endpoints: Pearson correlation coefficient for VBD compared to breast MRI
Retrospective clinical
n=10,804 cases
endpoints: Pearson correlation coefficient between CC and MLO views; mean absolute deviation between CC and MLO views
Retrospective clinical
n=10,804 cases
endpoints: Pearson correlation coefficient between right and left breast VBD; mean absolute deviation between right and left breast VBD
Retrospective clinical
n=433 cases
endpoints: Pearson correlation coefficient between VBD values on FFDM and DBT acquisitions; mean absolute deviation between VBD values on FFDM and DBT acquisitions; quadratically weighted kappa for four category DG values for DM and DBT
Reader study (MRMC)
n=800 patients
endpoints: Overall accuracy for four breast density categories; Cohen's weighted kappa for four breast density categories; Overall accuracy for dense vs non-dense assessment; Cohen's weighted kappa for dense vs non-dense assessment; sensitivity for dense vs. non-dense classification; specificity for dense vs. non-dense classification
Reported performance (7 observations)
source quote (p.11)
“For dense vs. non-dense classification, Transpara Density has a sensitivity of 87.3% [95% CI: 83.6% - 90.3%]”
source quote (p.11)
“and a specificity of 90.4% [95% CI: 87.2% - 92.9%].”
source quote (p.11)
“There was a high agreement in the four category DG values for DM and DBT with a quadratically weighted kappa of 0.810 [95% CI: 0.787 - 0.835].”
source quote (p.11)
“Overall the accuracy was 70.8% [95% CI: 67.6% - 73.9%]”
source quote (p.11)
“[Cohen's weighted kappa = 0.74 [95% CI: 0.70 - 0.79] for the four breast density categories (a-b-c-d)”
source quote (p.11)
“and 88.9% [95% CI: 86.6% - 90.9%]”
source quote (p.11)
“[Cohen's weighted kappa = 0.78 [95% CI: 0.72 - 0.84]] for the dense vs non-dense assessment.”
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
- 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
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).