CINA

K221716

AVICENNA.AI · cleared 2022-11-22 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.5)

“Cina is a radiological computer-assisted triage and notification software device.”

Algorithmartificial intelligence algorithm

source quote (p.7)

“Cina uses an artificial intelligence algorithm to analyze images and highlight cases with detected (1) ICH or (2) LVO on a standalone Web application in parallel to the ongoing standard of care image interpretation.”

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=1,290 cases · 3 site(s)

endpoints: evaluate the software's performance; Sensitivity and Specificity were analyzed; time-to-notification

Reported performance (14 observations)

sensitivity0.914CI 95% CI: 87.2% – 94.5%

source quote (p.9)

“Sensitivity and Specificity for the "ICH” prioritization and triage application are 91.4% (95% CI: 87.2% – 94.5%) and 97.5% (95% CI: 95.8% – 98.6%), respectively.”

specificity0.975CI 95% CI: 95.8% – 98.6%

source quote (p.9)

“Sensitivity and Specificity for the "ICH” prioritization and triage application are 91.4% (95% CI: 87.2% – 94.5%) and 97.5% (95% CI: 95.8% – 98.6%), respectively.”

aurocas written: “auc”0.94

source quote (p.9)

“The ROC curve shows an AUC of 0.94, which is also the same as for the predicate device.”

sensitivityas written: “LVO Sensitivity”0.979CI 95% CI: 94.6% – 99.4%

source quote (p.9)

“Regarding the “LVO” prioritization and triage application, Sensitivity and Specificity of 97.9% (95% CI: 94.6% – 99.4%) and 97.6% (95% CI: 95.1% – 99%), respectively are observed.”

specificityas written: “LVO Specificity”0.976CI 95% CI: 95.1% – 99%

source quote (p.9)

“Regarding the “LVO” prioritization and triage application, Sensitivity and Specificity of 97.9% (95% CI: 94.6% – 99.4%) and 97.6% (95% CI: 95.1% – 99%), respectively are observed.”

aurocas written: “LVO AUC”0.98

source quote (p.9)

“The ROC curve shows an AUC of 0.98, which is also the same as for the predicate device.”

ppvas written: “ICH PPV (10% prevalence)”0.802

source quote (p.9)

“Table 1: PPV and NVP values for ICH and LVO image processing applications ... 10% 80.2 99.0 81.7 99.8”

npvas written: “ICH NPV (10% prevalence)”0.99

source quote (p.9)

“Table 1: PPV and NVP values for ICH and LVO image processing applications ... 10% 80.2 99.0 81.7 99.8”

ppvas written: “LVO PPV (10% prevalence)”0.817

source quote (p.9)

“Table 1: PPV and NVP values for ICH and LVO image processing applications ... 10% 80.2 99.0 81.7 99.8”

npvas written: “LVO NPV (10% prevalence)”0.998

source quote (p.9)

“Table 1: PPV and NVP values for ICH and LVO image processing applications ... 10% 80.2 99.0 81.7 99.8”

time_to_resultas written: “Cina-ICH Time-to-notification (Mean)”13.2CI ± 2.9 seconds

source quote (p.10)

“The Cina's "ICH” triage mean ± SD "time-to-notification” is estimated to 13.2 ± 2.9 seconds.”

time_to_resultas written: “Cina-ICH Time-to-notification (95% CI)”stated without valueCI [13.0, 13.4] seconds

source quote (p.10)

“Table 2: Time-to-notification for ICH and LVO image processing applications ... Cina-ICH (N = 814) ... Lower 95% CI (seconds) 13.0 ... Upper 95% CI (seconds) 13.4”

time_to_resultas written: “Cina-LVO Time-to-notification (Mean)”25.8CI ± 7.0 seconds

source quote (p.10)

“Regarding Cina - LVO triage application, the mean ± SD "time-to-notification” is estimated to 25.8 ± 7.0 seconds.”

time_to_resultas written: “Cina-LVO Time-to-notification (95% CI)”stated without valueCI [25.1, 26.4] seconds

source quote (p.10)

“Table 2: Time-to-notification for ICH and LVO image processing applications ... Cina-LVO (N = 476) ... Lower 95% CI (seconds) 25.1 ... Upper 95% CI (seconds) 26.4”

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) K240942 (decision 2024-09-12) from Avicenna.AI for a matching device line ("CINA-CSpine") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K240942
re_clearance
The FDA AI/ML device list shows a newer 510(k) K240612 (decision 2024-05-31) from Avicenna.AI for a matching device line ("CINA-VCF") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K240612
re_clearance
The FDA AI/ML device list shows a newer 510(k) K233342 (decision 2024-03-15) from Avicenna.AI for a matching device line ("CINA-ASPECTS") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K233342
re_clearance
The FDA AI/ML device list shows a newer 510(k) K233968 (decision 2024-03-13) from Avicenna.AI for a matching device line ("CINA-iPE") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K233968

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).