MammoScreen® (3)
K240301Therapixel · cleared 2024-08-01 · product code QDQ · Radiology
Premarket evidence — what FDA accepted
source quote (p.5)
“MammoScreen is a concurrent reading medical software device using artificial intelligence to assist radiologists in the interpretation of mammograms.”
source quote (p.7)
“For both devices, a choice of medical image processing and machine learning techniques are implemented. The systems includes 'deep learning' modules for the detection of suspicious findings. These modules are trained with large databases of biopsy-proven examples of breast cancer and normal tissue.”
source quote (p.7)
“The algorithmic components have been updated to improve detection accuracy for the analysis.”
Validation studies (2)
Reader study (MRMC)
n=240 cases · 3 site(s)
endpoints: Whether the performance of radiologists when using MammoScreen 3 is superior to unaided radiologist for interpretation of screening mammograms (primary objective).; Whether the performance of MammoScreen 3 standalone is superior to unaided radiologist performance.; Whether the performance of MammoScreen 3 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.
Retrospective clinical
n=7,544 cases · 3 site(s)
endpoints: Standalone performances in breast cancer detection at the mammogram level overall and for all subgroups evaluated are summarized in the following table:; Standalone performances in lesion type assessment were measured in terms of Positive Percentage Agreement (PPA) and Negative Percentage Agreement (NPA) against the reference standard.; Standalone performances in CC/MLO quadrant assessment were measured in terms of Positive Percentage Agreement (PPA) and Negative Percentage Agreement (NPA) against the reference standard.; Standalone performances in depth assessment were measured in terms of Positive Percentage Agreement (PPA) and Negative Percentage Agreement (NPA) against the reference standard.
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 (3 observations)
source quote (p.11)
“Overall Sensitivity (95% CI) 0.912 (0.895, 0.928)”
source quote (p.11)
“Overall Specificity (95% CI) 0.727 (0.715, 0.740)”
source quote (p.11)
“Overall AUROC (95% CI) 0.927 (0.911, 0.942)”
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) 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
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).