EFAI ChestSuite XR Pneumothorax Assessment System

K221552

Ever Fortune AI Co., Ltd. · cleared 2022-11-08 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
EFAI ChestSuite XR Pneumothorax Assessment System, herein referred to as EFAI PNXXR, is a radiological computer-assisted triage and notification software system.
Algorithmartificial intelligence algorithm, deep learning techniques
source quote (p.3)
EFAI PNXXR analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI PNXXR is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumothorax or otherwise preclude clinical assessment of X-Ray cases. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pneumothorax to be identified.
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”(2005), and “Content of Premarket Submission for Management of Cybersecurity in Medical Devices."

Validation studies (1)

Retrospective clinical

n=800 images · 4 site(s)

endpoints: pneumothorax classification performance; processing time

standards: IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”(2005), Content of Premarket Submission for Management of Cybersecurity in Medical Devices.

Reported performance (3 observations)

sensitivity0.97CI 0.94-0.99
source quote (p.8)
Overall, the EFAI PNXXR was able to demonstrate sensitivity and specificity of 0.97 (95% CI=0.94-0.99) and 0.98 (95% CI=0.96-0.99) respectively, as well as an AUC of 0.99 (95% CI=0.98-1.00), which is substantially equivalent to the predicate device (Behold.ai red dot™™ (K191556).
specificity0.98CI 0.96-0.99
source quote (p.8)
Overall, the EFAI PNXXR was able to demonstrate sensitivity and specificity of 0.97 (95% CI=0.94-0.99) and 0.98 (95% CI=0.96-0.99) respectively, as well as an AUC of 0.99 (95% CI=0.98-1.00), which is substantially equivalent to the predicate device (Behold.ai red dot™™ (K191556).
aurocas written: “auc0.99CI 0.98-1.00
source quote (p.8)
Overall, the EFAI PNXXR was able to demonstrate sensitivity and specificity of 0.97 (95% CI=0.94-0.99) and 0.98 (95% CI=0.96-0.99) respectively, as well as an AUC of 0.99 (95% CI=0.98-1.00), which is substantially equivalent to the predicate device (Behold.ai red dot™™ (K191556).

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). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K221552