CINA-CSpine

K240942

Avicenna.AI · cleared 2024-09-12 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“CINA-CSpine is a radiological computer aided triage and notification software indicated for use in the analysis of cervical spine CT images.”
Algorithmdeep learning algorithms
source quote (p.6)
“CINA-CSpine receives cervical spine CT scans... processes them using deep learning algorithms involving execution of multiple computational steps to identify the suspected positive findings compatible with acute cervical spine fractures...”
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=328 cases

endpoints: software's performance (Sensitivity and Specificity) in detecting cervical spine (CSpine) fractures

Reported performance (4 observations)

sensitivity0.903CI 84.5% - 94.5%
source quote (p.8)
“As a primary endpoint, the global Sensitivity and Specificity were found to be 90.3% [95%CI: 84.5% - 94.5%] and 91.9% [95%CI: 86.8% - 95.5%], respectively.”
specificity0.919CI 86.8% - 95.5%
source quote (p.8)
“As a primary endpoint, the global Sensitivity and Specificity were found to be 90.3% [95%CI: 84.5% - 94.5%] and 91.9% [95%CI: 86.8% - 95.5%], respectively.”
time_to_resultas written: “mean time-to-notification for all included cases”2.9CI 2.7 - 3.0
source quote (p.8)
“The mean [95% Cl] time-to-notification for all included cases (n = 328) was estimated to be 2.9 [95% CI: 2.7 - 3.0] minutes for CINA-CSpine.”
time_to_resultas written: “mean time-to-notification for only true positive cases”2.8CI 2.6 - 3.0
source quote (p.8)
“When taking into account only true positive cases (n = 140), the mean [95% CI] time-to-notification was 2.8 [95%CI: 2.6 - 3.0] minutes for CINA-CSpine and 3.9 [95%CI: 3.8 - 4.1] minutes for BriefCase, the defined 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/K240942