Chest-CAD

K210666

Imagen Technologies, Inc · cleared 2021-07-20 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Chest-CAD is a computer-assisted detection (CADe) software device designed to assist physicians in identifying suspicious regions of interest (ROIs) in adult chest X-rays. Chest-CAD detects suspicious ROIs by analyzing radiographs using deep learning algorithms for computer vision and provides relevant annotations to assist physicians with their interpretations. The subject device is a software-only device.”
Algorithmdeep learning algorithms for computer vision / Artificial Neural Networks
source quote (p.5)
“Chest-CAD detects suspicious ROIs by analyzing radiographs using deep learning algorithms for computer vision and provides relevant annotations to assist physicians with their interpretations. Artificial Neural Networks”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Standalone

n=20,000 cases · 12 site(s)

Reader study (MRMC)

n=238 cases · 9 site(s)

endpoints: determine whether the accuracy of readers aided by Chest-CAD (“Aided”) was superior to the accuracy of readers when unaided by Chest-CAD (“Unaided”) as determined by the case-level, across-category aggregate Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve.

Reported performance (3 observations)

sensitivity0.908CI 0.905, 0.911
source quote (p.8)
“The results of the standalone testing demonstrated that Chest-CAD detects suspicious ROIs with high sensitivity (0.908; 95% Wilson's Confidence Interval: 0.905, 0.911)”
specificity0.887CI 0.885, 0.889
source quote (p.8)
“high specificity (0.887; 95% Wilson's Confidence Interval: 0.885, 0.889)”
aurocas written: “auc”0.976CI 0.975, 0.976
source quote (p.8)
“and high Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve (0.976, 95% Bootstrap Confidence Interval: 0.975, 0.976).”

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
-100%
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/K210666