BoneView

K212365

Gleamer · cleared 2022-03-01 · product code QBS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“BoneView is intended to analyze radiographs using machine learning techniques to identify and highlight fractures during the review of radiographs of:”
Algorithmmachine learning techniques, AI algorithm, Supervised Deep Learning
source quote (p.3)
“BoneView is intended to analyze radiographs using machine learning techniques to identify and highlight fractures during the review of radiographs of: Once received by BoneView, the radiographs are automatically processed by the AI algorithm to identify regions of interest. Supervised Deep Learning”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.10)
“Privacy HIPAA Compliant”

Validation studies (2)

Standalone

n=8,918 images

endpoints: Specificity; Sensitivity

Reader study (MRMC)

n=480 cases

endpoints: diagnostic accuracy (Specificity/Sensitivity pair)

Reported performance (4 observations)

sensitivity0.928CI 0.919 - 0.936
source quote (p.11)
“Specificity (with 95% Clopper-Pearson CI) and Sensitivity (with 95% Clopper-Pearson CI) of BoneView at the examination-level at the high-sensitivity operating point and high-specificity operating point on the merged datasets 0.928 [0.919 - 0.936]”
specificity0.932CI 0.925 -0.939
source quote (p.11)
“Specificity (with 95% Clopper-Pearson CI) and Sensitivity (with 95% Clopper-Pearson CI) of BoneView at the examination-level at the high-sensitivity operating point and high-specificity operating point on the merged datasets 0.932 [0.925 -0.939]”
specificityas written: “Reader Specificity (aided)”0.956CI 0.951-0.960
source quote (p.14)
“Reader specificity improved significantly from 0.906 (95% bootstrap CI: 0.898-0.913) to 0.956 (95% bootstrap CI: 0.951-0.960): +5% increase of the Specificity”
sensitivityas written: “Reader Sensitivity (aided)”0.752CI 0.745-0.759
source quote (p.14)
“Reader sensitivity improved significantly from 0.648 (95% bootstrap CI: 0.640-0.656) to 0.752 (95% bootstrap CI: 0.745-0.759): +10.4% increase of the Sensitivity”

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
1
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K222176 (decision 2023-03-02) from Gleamer for a matching device line ("BoneView") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K222176

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/K212365