Median LCS (internal name) / eyonis LCS (trade name) (1.0)

K251474

Median Technologies · cleared 2026-02-06 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
eyonis® LCS is an AI/ML technology-based end-to-end CADe/CADx Software as Medical Device (SaMD) intended to allow early detection, localization and characterization of pulmonary parenchymal nodules from LDCT DICOM images produced during Chest CT examinations.
Algorithmdeep learning modules
source quote (p.8)
The device includes ‘deep learning’ modules for recognition of suspicious lesions.
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.9)
Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions

Validation studies (2)

Standalone

n=1,147 patients · 7 site(s)

endpoints: patient-level AUROC; sensitivity at COT; specificity at COT; AULROC; FROC analysis

standards: IEC 62366-1, ISO 20417, ISO 14971, IEC 62304, IEC 82304-1, ISO 15223-1, NEMA PS 3.1

Reader study (MRMC)

n=480 images

endpoints: image-level Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve; Sensitivity; Specificity; inter-reader agreement per patient score; inter-reader agreement per patient management

Reported performance (8 observations)

sensitivity84.5CI 80.22-88.17
source quote (p.10)
a sensitivity at COT of 84.50% [80.22-88.17] p<0.0001
specificity80.25CI 77.33-82.95
source quote (p.10)
a specificity at COT of 80.25% [77.33-82.95] p<0.0001
aurocas written: “auc0.904CI 0.881-0.926
source quote (p.10)
a patient-level AUROC of 0.904 [0.881-0.926] p<0.0001
aurocas written: “AULROC0.869CI 0.843-0.894
source quote (p.10)
an AULROC of 0.869 [0.843-0.894] p<0.0001
sensitivityas written: “FROC sensitivity at COT80.59CI 76.20-84.49
source quote (p.10)
FROC analysis yielded a sensitivity at COT of 80.59% [76.20-84.49]
false_positive_rate_per_imageas written: “FROC false-positive rate per scan0.271CI 0.235-0.313
source quote (p.10)
a false-positive rate of 0.271 [0.235-0.313] per scan.
aurocas written: “Reader Study Aided AUC0.8434
source quote (p.11)
aided AUC = 0.8434
aurocas written: “Reader Study Delta AUC (aided - unaided)0.0158CI 0.0032-0.0288
source quote (p.11)
∆AUC (aided – unaided) = 0.0158 [0.0032-0.0288]

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). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K251474