uAI EasyTriage-Rib

K193271

Shanghai United Imaging Intelligence Co., Ltd. · cleared 2021-01-15 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“uAI EasyTriage-Rib is a radiological computer-assisted triage and notification software device for analysis of CT chest images.”
Algorithmartificial intelligence algorithm, deep learning algorithm
source quote (p.3)
“uAI EasyTriage-Rib uses an artificial intelligence algorithm to analyze images and highlight studies with suspected multiple (3 or more) acute rib fractures in a standalone application for study list prioritization or triage in parallel to ongoing standard of care. Specifically, the subject and predicate software utilize a deep learning algorithm trained on medical 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=200 cases

endpoints: evaluate the software's performance in identifying CT chest images containing multiple (3 or more) acute rib fractures

Reported performance (4 observations)

sensitivity0.927CI 95% CI: 84.8%-97.3%
source quote (p.9)
“The sensitivity was 92.7% (95% CI: 84.8%-97.3%)”
specificity0.847CI 95% CI: 77.0%-90.7%
source quote (p.9)
“and specificity was 84.7% (95% CI: 77.0%-90.7%).”
aurocas written: “auc”0.939CI 95% CI: 0.906, 0.972
source quote (p.9)
“The AUC was 0.939 (95% CI: 0.906, 0.972).”
time_to_resultas written: “Time-to-notification (uAI EasyTriage-Rib)”69.56
source quote (p.10)
“As shown in the table below, the average time-to-notification of uAI EasyTriage-Rib among 76 true positive studies 69.56 seconds is comparable to the time-to-notification of the HealthVCF software documented for an average of 47.36 seconds, suggesting that the radiologist can receive a notification timely on the status of studies with potential rib fracture findings with the help of uAI EasyTriage-Rib.”

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/K193271