Rayvolve

K240845

AZmed SAS · cleared 2024-07-17 · product code QBS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system. Rayvolve is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays.”
AlgorithmSupervised Deep learning object detection model
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. Machine learning technology: Supervised Deep learning. The core design of the Rayvolve algorithm, including the object detection model, remains unchanged from the predicate device (Rayvolve K220164).”
Adaptive (vs locked)No
source quote (p.15)
“The training dataset for the subject device was expanded to include 150,000 osteoarticular radiographs, compared to 115,000 in the predicate device. This expansion was undertaken to enhance the algorithm's robustness by including a more comprehensive representation of pediatric cases alongside adult cases. The algorithm was retrained using the expanded dataset, which involved adjusting the model weights to optimize performance across the broader dataset that includes both adult and pediatric populations.”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.9)
“Privacy: HIPAA Compliant”

Validation studies (3)

Bench

sample size not stated

Standalone

n=3,016 images

endpoints: sensitivity; specificity; AUC

Reader study (MRMC)

n=186 cases

endpoints: diagnostic accuracy (AUC); sensitivity; specificity

Reported performance (3 observations)

sensitivity0.9611CI 0.9480; 0.9710
source quote (p.11)
“The results of standalone testing demonstrated that Rayvolve detects fractures of the musculoskeletal system radiographs with high sensitivity (0.9611, 95% Wilson's Confidence Interval (CI): 0.9480; 0.9710)”
specificity0.8597CI 0.8434; 0.8745
source quote (p.11)
“high specificity (0.8597; 95% Wilson's CI: 0.8434; 0.8745)”
aurocas written: “auc”0.9399CI 0.9330; 0.9470
source quote (p.11)
“and high Area Under The Curve (AUC) of the Receiver Operating Characteristic (ROC) (0.9399; 95% Bootstrap CI: 0.9330; 0.9470).”

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 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/K240845