CINA CHEST

K210237

Avicenna.AI · cleared 2021-05-19 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“CINA CHEST is a radiological computer aided triage and notification software indicated for use in the analysis of Chest and Thoraco-abdominal CT angiography.”
Algorithmartificial intelligence algorithm, deep learning AI algorithms
source quote (p.3)
“CINA CHEST uses an artificial intelligence algorithm to analyze images and highlight cases with detected PE and AD on a standalone Web application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected PE or AD findings. Notifications include compressed preview images that are meant for informational purposes only, and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device. software packages with similar technological characteristics and principles of operation, and incorporate deep learning AI algorithms that process images, and software to send notifications and to display unannotated preview images.”
Adaptive (vs locked)No
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Retrospective clinical

n=694 cases

endpoints: Sensitivity; Specificity

Reported performance (8 observations)

sensitivityas written: “Sensitivity (PE)”91.1CI 86.1% – 94.7%
source quote (p.7)
“Sensitivity and Specificity for the “PE” prioritization and triage application were found to be 91.1% [95% CI: 86.1% – 94.7%] and 91.8% [95% CI: 87.1% – 95.1%], respectively.”
specificityas written: “Specificity (PE)”91.8CI 87.1% – 95.1%
source quote (p.7)
“Sensitivity and Specificity for the “PE” prioritization and triage application were found to be 91.1% [95% CI: 86.1% – 94.7%] and 91.8% [95% CI: 87.1% – 95.1%], respectively.”
sensitivityas written: “Sensitivity (AD)”96.4CI 91.7% – 98.8%
source quote (p.7)
“Regarding the "AD” prioritization and triage application, Sensitivity and Specificity of 96.4% [95% CI: 91.7% – 98.8%] and 97.5% [95% CI: 93.8% – 99.3%], respectively, were obtained.”
specificityas written: “Specificity (AD)”97.5CI 93.8% – 99.3%
source quote (p.7)
“Regarding the "AD” prioritization and triage application, Sensitivity and Specificity of 96.4% [95% CI: 91.7% – 98.8%] and 97.5% [95% CI: 93.8% – 99.3%], respectively, were obtained.”
accuracyas written: “Accuracy (PE)”91.4
source quote (p.8)
“The results of the standalone assessment study demonstrated an overall agreement (Accuracy) of 91.4% and 97% for the “PE” and "AD" tested cases, respectively, when compared to the ground truth (operators' visual assessments).”
accuracyas written: “Accuracy (AD)”97
source quote (p.8)
“The results of the standalone assessment study demonstrated an overall agreement (Accuracy) of 91.4% and 97% for the “PE” and "AD" tested cases, respectively, when compared to the ground truth (operators' visual assessments).”
time_to_resultas written: “Mean time-to-notification (PE)”63CI 61.5 – 64.6
source quote (p.8)
“Specifically, mean “time-to-notification” were estimated to be 63 [95% CI: 61.5 – 64.6] seconds and 36.5 [95% CI: 35.4 – 37.5] seconds for CINA CHEST – PE and CINA CHEST – AD, respectively.”
time_to_resultas written: “Mean time-to-notification (AD)”36.5CI 35.4 – 37.5
source quote (p.8)
“Specifically, mean “time-to-notification” were estimated to be 63 [95% CI: 61.5 – 64.6] seconds and 36.5 [95% CI: 35.4 – 37.5] seconds for CINA CHEST – PE and CINA CHEST – AD, respectively.”

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) K221716 (decision 2022-11-22) from AVICENNA.AI for a matching device line ("CINA") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K221716

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/K210237