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. 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.”
Algorithmartificial intelligence algorithm, deep learning techniques
source quote (p.3)
“EFAI PNXXR analyzes cases using an artificial intelligence algorithm to identify suspected findings. 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
PCCPNo
Cybersecurity addressedYes
source quote (p.8)
“Content of Premarket Submission for Management of Cybersecurity in Medical Devices.”

Validation studies (1)

Retrospective clinical

n=800 images · 4 site(s)

endpoints: Sensitivity; Specificity; AUC

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”
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”
aurocas written: “auc”0.99CI 0.98-1.00
source quote (p.8)
“as well as an AUC of 0.99 (95% CI=0.98-1.00)”

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/K221552