BoneView
K212365Gleamer · cleared 2022-03-01 · product code QBS · Radiology
Premarket evidence — what FDA accepted
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: BoneView is intended for use as a concurrent reading aid during the interpretations of radiographs. BoneView is for prescription use only and is indicated for adults only.”
source quote (p.3)
“BoneView is intended to analyze radiographs using machine learning techniques to identify and highlight fractures during the review of radiographs. Once received by BoneView, the radiographs are automatically processed by the AI algorithm to identify regions of interest. Machine Learning Methodology: Supervised Deep Learning”
source quote (p.6)
“The training of BoneView was performed on a training dataset of 44,649 radiographs, representing 151,096 images (52.4% of males, with age: range [0 – 109]; mean 42.4 +/- 24.6) for all anatomical areas of interest in the Indications for Use and from various manufacturers.”
source quote (p.9)
“Privacy: HIPAA Compliant”
Validation studies (2)
Bench
n=8,918 images
endpoints: sensitivity; specificity
Reader study (MRMC)
n=480 cases
endpoints: diagnostic accuracy; specificity; sensitivity
Reported performance (6 observations)
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”
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”
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”
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”
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”
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”
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) 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). 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).