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. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing trauma studies with suspected positive findings of multiple (3 or more) acute rib fracture(s). 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. The user is presented with notifications of cases with suspected findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.”

Algorithmdeep learning algorithm trained on medical images

source quote (p.7)

“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; potential clinical benefit of worklist prioritization

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%) and specificity was 84.7% (95% CI: 77.0%-90.7%).”

specificity0.847CI 95% CI: 77.0%-90.7%

source quote (p.9)

“The sensitivity was 92.7% (95% CI: 84.8%-97.3%) 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”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 61.36 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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

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).