BunkerHill BMD
K242295BunkerHill Health · cleared 2025-04-08 · product code KGI · Radiology
Premarket evidence — what FDA accepted
source quote (p.5)
“The Bunkerhill BMD application is a software only medical device (SaMD) that includes deep- learning-based computer vision and post-processing algorithms that estimates the bone mineral density from previously obtained computed tomography (CT) images.”
source quote (p.5)
“The Bunkerhill BMD application is a software only medical device (SaMD) that includes deep- learning-based computer vision and post-processing algorithms that estimates the bone mineral density from previously obtained computed tomography (CT) images.”
source quote (p.8)
“Safety and performance of the Bunkerhill BMD has been evaluated and verified in accordance with software specifications and applicable performance standards through Software Development and Validation & Verification Process to ensure performance according to specifications, User Requirements and Federal Regulations and Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices” and Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.”
Validation studies (1)
Retrospective clinical
n=371 cases · 4 site(s)
endpoints: Sensitivity; Specificity
Reported performance (5 observations)
source quote (p.8)
“The Bunkerhill BMD algorithm achieved a sensitivity of 81.0 (74.0 - 86.8) and specificity of 78.4 (72.3 - 83.7), which passed the acceptance criteria for the primary endpoint with lower bound 95% confidence interval of both Sensitivity and Specificity being greater than 70%.”
source quote (p.8)
“The Bunkerhill BMD algorithm achieved a sensitivity of 81.0 (74.0 - 86.8) and specificity of 78.4 (72.3 - 83.7), which passed the acceptance criteria for the primary endpoint with lower bound 95% confidence interval of both Sensitivity and Specificity being greater than 70%.”
source quote (p.8)
“Additionally, the device achieved was evaluated across multiple secondary metrics, including a Pearson correlation coefficient of 0.791 (95% CI: 0.752–0.830), AUROC of 0.883 (95% CI: 0.849–0.916), PPV of 73.6% (95% CI: 66.4%–79.9%), and NPV of 84.8% (95% CI: 79.0%–89.5%), further supporting the robustness and reliability of the algorithm.”
source quote (p.8)
“Additionally, the device achieved was evaluated across multiple secondary metrics, including a Pearson correlation coefficient of 0.791 (95% CI: 0.752–0.830), AUROC of 0.883 (95% CI: 0.849–0.916), PPV of 73.6% (95% CI: 66.4%–79.9%), and NPV of 84.8% (95% CI: 79.0%–89.5%), further supporting the robustness and reliability of the algorithm.”
source quote (p.8)
“Additionally, the device achieved was evaluated across multiple secondary metrics, including a Pearson correlation coefficient of 0.791 (95% CI: 0.752–0.830), AUROC of 0.883 (95% CI: 0.849–0.916), PPV of 73.6% (95% CI: 66.4%–79.9%), and NPV of 84.8% (95% CI: 79.0%–89.5%), further supporting the robustness and reliability of the algorithm.”
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
- recall_reason_pattern
Software/algorithm-related recall in product code KGI (Medimaps Group Fongit Chemin des Aulx 18 Plan-les-Ouates Switzerland, initiated 2025-02-03): "Potential variability in calculations from fast array scans compared to array scans when operating on Hologic Horizon machines." Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:96233
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).