CINA-VCF

K240612

Avicenna.AI · cleared 2024-05-31 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“CINA-VCF is a radiological computer aided triage and notification software indicated for use in patients aged 50 years and over undergoing non-enhanced or contrast-enhanced CT scans which include the chest and/or abdomen.”
Algorithmartificial intelligence algorithm, deep learning models
source quote (p.3)
“CINA-VCF uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone application in parallel to the ongoing standard of care image interpretation. The device uses deep learning models to detect VCF at the T1-L5 level.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=474 cases

endpoints: software's performance in identifying vertebral compression fractures (VCF); ROC AUC; Sensitivity; Specificity; Accuracy; CINA-VCF time-to-notification; CINA-VCF prioritization and triage effectiveness

standards: DICOM (Digital Imaging and Communications in Medicine) – Developed by the American College of Radiology and the National Electrical Manufacturers Association. NEMA PS 3.1 - 3.20.

Reported performance (6 observations)

sensitivity95.2CI 90.7% – 97.9%
source quote (p.7)
“Sensitivity and Specificity were 95.2% [95% CI: 90.7% – 97.9%] and 92.9% [95% CI: 89.4% – 96.5%], respectively.”
specificity92.9CI 89.4% – 96.5%
source quote (p.7)
“Sensitivity and Specificity were 95.2% [95% CI: 90.7% – 97.9%] and 92.9% [95% CI: 89.4% – 96.5%], respectively.”
aurocas written: “auc”0.974CI 0.962 - 0.986
source quote (p.7)
“The ROC AUC was 0.974 [95% CI: 0.962 - 0.986], which exceeded the 0.95 performance goal, thus, achieving the primary endpoint.”
accuracyas written: “Accuracy”93.7CI 91.1% - 95.7%
source quote (p.7)
“Similarly, the overall agreement (Accuracy) was 93.7% [95% CI: 91.1% - 95.7%], which represents very good predictions.”
time_to_resultas written: “Time-to-Notification (All cases)”23.4CI 22.7 – 24.2
source quote (p.7)
“The mean [95% Cl] time-to-notification for all included cases (n = 474) was estimated to be 23.4 [95% CI: 22.7 – 24.2] seconds for CINA-VCF.”
time_to_resultas written: “Time-to-Notification (True Positive cases)”21.7CI 20.5 – 22.9
source quote (p.8)
“When taking into account only true positive cases (n = 158), the mean [95% CI] time-to-notification was 21.7 [95% CI: 20.5 – 22.9] seconds for CINA-VCF and 117.2 [95% CI: 98.64 – 135.85] seconds for BriefCase, the selected predicate device.”

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/K240612