Rayvolve

K220164

AZmed SAS · cleared 2022-06-02 · product code QBS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.7)
“The medical device is called Rayvolve. It is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays. Rayvolve is intended to be used as an aided-diagnosis device and does not operate autonomously.”
Algorithmdeep learning techniques / Supervised Deep Learning
source quote (p.7)
“It is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Standalone

n=2,626 images · 4 site(s)

endpoints: characterize the detection accuracy of Rayvolve for detecting adult patient fractures; demonstrate Rayvolve's ability to perform across different subgroup variables; compute Rayvolve AUC, sensitivity, and specificity for all the potential and relevant observable subgroups such as gender, age, anatomic region, machine acquisition, machine view, as well as Rayvolve performances depending on weight-bearing and complex & uncommon cases

Retrospective clinical

n=186 cases

endpoints: determine whether the diagnostic accuracy of readers aided by Rayvolve is superior to reader accuracy when unaided by Rayvolve, as determined by the AUC of the ROC curve; report the sensitivity and the specificity of the Rayvolve-aided and unaided reads

Reported performance (3 observations)

sensitivity0.98763CI 0.97559; 0.99421
source quote (p.13)
“high sensitivity (0.98763, 95% Wilson's Confidence Interval (CI): 0.97559; 0.99421)”
specificity0.88558CI 0.87119; 0.89882
source quote (p.13)
“high specificity (0.88558; 95% Wilson's CI: 0.87119; 0.89882)”
aurocas written: “auc”0.98607CI 0.98104; 0.99058
source quote (p.13)
“high Area Under The Curve (AUC) of the Receiver Operating Characteristic (ROC) (0.98607; 95% Bootstrap CI: 0.98104; 0.99058)”

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) K240845 (decision 2024-07-17) from AZmed SAS for a matching device line ("Rayvolve") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K240845

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/K220164