CINA

K200855

AVICENNA.AI · cleared 2020-06-24 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.3)

“CINA is a radiological computer aided triage and notification software indicated for use in the analysis of (1) non-enhanced head CT images and (2) CT angiographies of the head.”

Algorithmdeep learning AI algorithms

source quote (p.9)

“All devices are 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)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=814 cases · 3 site(s)

endpoints: sensitivity; specificity; time-to-notification

Reported performance (14 observations)

sensitivity91.4CI 95% CI: 87.2% – 94.5%

source quote (p.7)

“Sensitivity and specificity for the “ICH” prioritization and triage application were observed to be 91.4% (95% CI: 87.2% – 94.5%) and 97.5% (95% CI: 95.8% – 98.6%), respectively.”

specificity97.5CI 95% CI: 95.8% – 98.6%

source quote (p.7)

“Sensitivity and specificity for the “ICH” prioritization and triage application were observed to be 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.7)

“ROC curve showed an AUC of 0.94.”

accuracyas written: “accuracy (ICH)”95.6

source quote (p.7)

“The results of the standalone assessment study demonstrated an overall agreement (accuracy) of 95.6% and 97.7% for the “ICH” and "LVO” tested cases, respectively, when compared to the ground truth (operators' visual assessments).”

accuracyas written: “accuracy (LVO)”97.7

source quote (p.7)

“The results of the standalone assessment study demonstrated an overall agreement (accuracy) of 95.6% and 97.7% for the “ICH” and "LVO” tested cases, respectively, when compared to the ground truth (operators' visual assessments).”

sensitivityas written: “Sensitivity (LVO)”97.9CI 95% CI: 94.6% – 99.4%

source quote (p.7)

“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 were observed.”

specificityas written: “Specificity (LVO)”97.6CI 95% CI: 95.1% – 99%

source quote (p.7)

“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 were observed.”

aurocas written: “AUC (LVO)”0.98

source quote (p.7)

“ROC curve showed an AUC of 0.98.”

ppvas written: “PPV (ICH) at 10% prevalence”80.2

source quote (p.8)

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

npvas written: “NPV (ICH) at 10% prevalence”99

source quote (p.8)

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

ppvas written: “PPV (LVO) at 10% prevalence”81.7

source quote (p.8)

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

npvas written: “NPV (LVO) at 10% prevalence”99.8

source quote (p.8)

“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: “Time-to-Notification (ICH) Mean”21.6CI ± 4.4

source quote (p.8)

“Table 2: Time-to-notification for ICH and LVO image processing applications ... CINA - ICH 21.6 ± 4.4 20.4 14.4 53.3”

time_to_resultas written: “Time-to-Notification (LVO) Mean”34.7CI ± 10.7

source quote (p.8)

“Table 2: Time-to-notification for ICH and LVO image processing applications ... CINA - LVO 34.7 ± 10.7 33.4 14.3 63.3”

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
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
re_clearance
The FDA AI/ML device list shows a newer 510(k) K210237 (decision 2021-05-19) from Avicenna.AI for a matching device line ("CINA CHEST") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K210237

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).