Auto Lung Nodule Detection

K201560

Samsung Electronics Co.,Ltd. · cleared 2021-08-31 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“The Auto Lung Nodule Detection is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 10 to 30 mm in size.”
AlgorithmComputer-Aided Detection (CADe) using machine learning
source quote (p.5)
“Auto Lung Nodule Detection is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in Chest X-ray images for indication of locations for high nodule probability, which has an effective detection sizes from 10 mm to 30 mm. Technology: Machine learning.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Reader study (MRMC)

sample size not stated

endpoints: sensitivity; false positives per image (FPPI); jackknife alternative free response receiver operating characteristic (JAFROC) figure of merit (FOM)

standards: ISO14971:2007, IEC62304:2006, IEC62366-1:2015

Reported performance (1 observation)

false_positive_rate_per_imageas written: “false positives per image (FPPI)”stated without value
source quote (p.7)
“Nodule detection performances before and after ALND were measured via sensitivity, false positives per image (FPPI), and jackknife alternative free response receiver operating characteristic (JAFROC) figure of merit (FOM).”

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/K201560