Bunkerhill ECG-EF

K250649

BunkerHill Health · cleared 2025-09-19 · product code QYE · Cardiovascular

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“ECG-EF is a software-only medical device that employs deep learning algorithms to analyze 12-lead ECG data for the detection of low left ventricular ejection fraction (LVEF < 40%).The subject device and the predicate device are Software as a Medical Device (SaMD) provided as a software module packaged in a Docker container.”
Algorithmdeep learning algorithms, machine learning model
source quote (p.6)
“ECG-EF is a software-only medical device that employs deep learning algorithms to analyze 12-lead ECG data for the detection of low left ventricular ejection fraction (LVEF < 40%). ECG-EF algorithm receives digital 12-lead ECG data and processes it through its machine learning model.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.10)
“a thorough cybersecurity assessment was performed per FDA Guidance "Cybersecurity in medical devices: Quality System Considerations and Content of Premarket Submissions".”

Validation studies (1)

Retrospective clinical

n=15,994 patients · 5 site(s)

endpoints: Detection of Low Left Ventricular Ejection Fraction; AUROC plot

standards: Software Development and Validation & Verification Process, User Requirements and Federal Regulations, Content of Premarket Submissions for Device Software Functions, Cybersecurity in medical devices: Quality System Considerations and Content of Premarket Submissions

Reported performance (4 observations)

sensitivity82.66CI 80.90–84.30
source quote (p.11)
“The Bunkerhill ECG-EF device achieved a sensitivity of 82.66% (80.90–84.30)”
specificity83.2CI 82.60–83.80
source quote (p.11)
“a specificity of 83.20% (82.60–83.80)”
ppvas written: “PPV”37.2CI 35.70–38.76
source quote (p.11)
“a PPV of 37.20% (35.70–38.76)”
npvas written: “NPV”97.54CI 97.28–97.83
source quote (p.11)
“and NPV of 97.54% (97.28–97.83)”

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
0
drift signals on this device

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 Cardiovascular 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/K250649