EFAI Bonesuite XR Bone Age Pro Assessment System (BAP-XR-100)

K234042

Ever Fortune.AI Co., Ltd. · cleared 2024-06-07 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“The device is a software designed to aid the quantification of bone age for patients between 2 to 16 years old. The software uses deep learning techniques to analyze posterior-anterior (PA) radiographs of the left-hand according to the Greulich-Pyle (GP) method.”
Algorithmdeep learning techniques to analyze posterior-anterior (PA) radiographs of the left-hand according to the Greulich-Pyle (GP) method.
source quote (p.5)
“The software uses deep learning techniques to analyze posterior-anterior (PA) radiographs of the left-hand according to the Greulich-Pyle (GP) method.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.7)
“Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, “Content of Premarket Submissions for Device Software Functions” and “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions."”

Validation studies (2)

Bench

n=2,644 cases

standards: IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, Content of Premarket Submissions for Device Software Functions, Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions

Retrospective clinical

n=600 cases · 27 site(s)

endpoints: intercept and slope of a Deming regression between GT and EFAI BAPXR's output; over 88% of the cases with a difference less than 0.5 years between GT and EFAI BAPXR's output

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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
3
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 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/K234042