QOCA image Smart CXR Image Processing System

K221868

Quanta Computer Inc. · cleared 2023-01-27 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“QOCA® image Smart CXR Image Processing System is a software as medical device (SaMD) used, through artificial intelligence/deep learning technology, to analyze chest X-ray images of adult patient, and then identify cases with suspected pneumothorax.”
Algorithmartificial intelligence/deep learning technology
source quote (p.4)
“QOCA® image Smart CXR Image Processing System is a software as medical device (SaMD) used, through artificial intelligence/deep learning technology, to analyze chest X-ray images of adult patient, and then identify cases with suspected pneumothorax.”
Adaptive (vs locked)No
source quote (p.7)
“This product, QOCA® image Smart CXR Image Processing System, is a web-based medical device using a locked artificial intelligence algorithm.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Retrospective clinical

n=3,105 images

Retrospective clinical

n=2,947 images

Reported performance (3 observations)

sensitivity0.925CI [90.5%, 94.2%]
source quote (p.12)
“The performance of the subject device across the performance assessment dataset achieves an area under the curve (AUC) of 97.8% (95% CI: [97.0%, 98.5%]; in addition, the sensitivity and specificity achieves 92.5% (95% CI: [90.5%, 94.2%]), 94.0% (95% CI: [93.9%, 94.6%]) respectively, without subgroup breakdown.”
specificity0.94CI [93.9%, 94.6%]
source quote (p.12)
“The performance of the subject device across the performance assessment dataset achieves an area under the curve (AUC) of 97.8% (95% CI: [97.0%, 98.5%]; in addition, the sensitivity and specificity achieves 92.5% (95% CI: [90.5%, 94.2%]), 94.0% (95% CI: [93.9%, 94.6%]) respectively, without subgroup breakdown.”
aurocas written: “auc”0.978CI [97.0%, 98.5%]
source quote (p.12)
“The performance of the subject device across the performance assessment dataset achieves an area under the curve (AUC) of 97.8% (95% CI: [97.0%, 98.5%]; in addition, the sensitivity and specificity achieves 92.5% (95% CI: [90.5%, 94.2%]), 94.0% (95% CI: [93.9%, 94.6%]) respectively, without subgroup breakdown.”

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/K221868