Lung-CAD

K230085

Imagen Technologies, Inc · cleared 2023-10-03 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of lung hyperinflation.”
Algorithmdeep learning algorithm; modern deep learning and computer vision techniques; Supervised Deep Learning
source quote (p.5)
“The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs. Supervised Deep Learning”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.7)
“HIPAA Compliant”

Validation studies (2)

Bench

n=5,000 cases

endpoints: sensitivity; specificity; AUC; FROC

Reader study (MRMC)

n=244 cases

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

Reported performance (6 observations)

sensitivity0.898CI 0.856, 0.929
source quote (p.8)
“The results of the standalone testing demonstrated that Lung-CAD detects ROIs with high sensitivity (0.898; 95% Wilson's Confidence Interval: 0.856, 0.929)”
specificity0.894CI 0.885, 0.902
source quote (p.8)
“high specificity (0.894; 95% Wilson's Confidence Interval: 0.885, 0.902)”
aurocas written: “auc”0.964CI 0.956, 0.972
source quote (p.8)
“high Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve (0.964, 95% Bootstrap Confidence Interval: 0.956, 0.972)”
ppvas written: “Positive Predictive Value”0.322CI 0.289, 0.357
source quote (p.9)
“Positive Predictive Value 0.322 (0.289, 0.357)”
npvas written: “Negative Predictive Value”0.994CI 0.991, 0.996
source quote (p.9)
“Negative Predictive Value 0.994 (0.991, 0.996)”
aurocas written: “Reader AUC improvement”0.0632CI 0.0632, 0.0633
source quote (p.10)
“Reader AUC improvement for lung hyperinflation was 0.0632 (95% Confidence Interval: 0.0632, 0.0633).”

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/K230085