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.”

Algorithmdeep learning techniques

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.”

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)

Bench

n=2,626 images · 4 site(s)

endpoints: to characterize the detection accuracy of Rayvolve for detecting adult patient fractures.; to demonstrate Rayvolve's ability to perform across different subgroup variables. More precisely, the goal is to 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.

Reader study (MRMC)

n=186 cases

endpoints: to determine whether the diagnostic accuracy of readers aided by Rayvolve (“Rayvolve-aided”) is superior to the diagnostic accuracy of readers unaided by Rayvolve (“Rayvolve-unaided") as determined by the AUC of the Receiver Operating Characteristic (ROC) Curve.; to 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)

“The results of standalone testing demonstrated that Rayvolve detects fractures of the musculoskeletal system radiographs with 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)

“and 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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

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). 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).