JBS-LVO

K241480

JLK, Inc. · cleared 2024-09-27 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“JBS-LVO is a radiological computer aided triage and notification (CADt) software package compliant with the DICOM standard.”
Algorithmartificial intelligence (AI) software algorithm utilizing convolutional neural network (CNN)
source quote (p.6)
“The JBS-LVO Image Analysis Algorithm (LVO Detection Algorithm) is a locked, artificial intelligence (AI) software algorithm utilizing convolutional neural network (CNN) that analyzes CTA images of the brain for a suspected LVO.”
Adaptive (vs locked)No
source quote (p.6)
“The JBS-LVO Image Analysis Algorithm (LVO Detection Algorithm) is a locked, artificial intelligence (AI) software algorithm utilizing convolutional neural network (CNN) that analyzes CTA images of the brain for a suspected LVO.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

sample size not stated

endpoints: sensitivity; specificity; time-to-notification

Reported performance (4 observations)

sensitivity91.8CI 95% confidence interval (CI) of 85.8% to 95.8%
source quote (p.7)
“Specifically, the sensitivity was 91.8% with a 95% confidence interval (CI) of 85.8% to 95.8%.”
specificity92.8CI 95% CI of 87.2% to 96.5%
source quote (p.7)
“The specificity was 92.8% with a 95% CI of 87.2% to 96.5%.”
aurocas written: “auc”95.6CI 95% CI of 93.0% to 98.1%
source quote (p.7)
“The area under the curve (AUC) was 95.6% with a 95% CI of 93.0% to 98.1%.”
time_to_resultas written: “time-to-notification (mean)”2.95CI 95% CI: 2.89 - 3.02
source quote (p.7)
“The total CTA-to-notification time for the JBS-LVO system ranged from 2.32 to 3.29 minutes, with a mean time of 2.95 minutes (95% CI: 2.89 - 3.02).”

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/K241480