Visage Breast Density

K201411

Visage Imaging GmbH · cleared 2021-01-29 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.3)

“Visage Breast Density is a software application intended for use with compatible full field digital mammography and digital breast tomosynthesis systems.”

AlgorithmThe device employs a convolutional neural network (CNN) for the automatic classification of breast density. The CNN has been trained on a large database of mammography exams. When applied to a mammography image, the CNN computes four likelihoods corresponding to the four breast density categories. The classifications of the individual images are merged into a general classification of the mammography study.

source quote (p.5)

“Visage Breast Density employs a convolutional neural network (CNN) for the automatic classification of breast density. The CNN has been trained on a large database of mammography exams. When applied to a mammo-graphy image, the CNN computes four likelihoods corresponding to the four breast density categories. The classifications of the individual images are merged into a general classification of the mammography study.”

Adaptive (vs locked)No

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=1,200 cases · 2 site(s)

endpoints: accuracies per category for ACR BI-RADS Atlas 5th Edition; total accuracy for ACR BI-RADS Atlas 5th Edition; accuracy for binary classification 'dense' versus 'non-dense'

Reported performance (1 observation)

accuracyas written: “total accuracy”stated without value

source quote (p.6)

“The accuracies per category and the total accuracy were computed for the classification into the four categories of the ACR BI-RADS Atlas 5th Edition as well as for the binary classification 'dense' versus 'non-dense'.”

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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

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).