InferRead Lung CT.AI

K240554

Infervision Medical Technology Co., Ltd. · cleared 2025-05-16 · product code OEB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“InferRead Lung CT.AI uses the deep learning (DL) technology to perform nodule detection. It is a dedicated post-processing application that generates CADe marks as an overlay on original CТ scans. The software can be installed in a healthcare facility or a cloud-based platform and is comprised of computer-assisted reading tools designed to aid radiologists in detecting, segmenting, measuring and localizing actionable pulmonary nodules that are 4mm or above during the review of chest CT examinations of asymptomatic populations, with enhanced capabilities for pulmonary nodule follow-up comparison and lung analysis.”
Algorithmdeep learning (DL) technology
source quote (p.6)
“InferRead Lung CT.AI uses the deep learning (DL) technology to perform nodule detection.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.10)
“Cybersecurity and vulnerability analyses were conducted, and it has been determined that InferRead Lung CT.AI conforms to the cybersecurity requirements.”

Validation studies (3)

Retrospective clinical

n=98 patients

endpoints: overall nodule Match Rate

Retrospective clinical

n=94 patients

endpoints: overall Lobe Localization Accuracy Rate

Retrospective clinical

n=22 patients

endpoints: average Dice Coefficient

Reported performance (2 observations)

accuracyas written: “Lobe Localization Accuracy Rate”0.957CI 95%CI: 0.929-0.986
source quote (p.9)
“InferRead Lung CT.AI achieved an overall Lobe Localization Accuracy Rate of 0.957 (95%CI: 0.929-0.986).”
diceas written: “average Dice Coefficient”0.966CI 95%CI: 0.962 to 0.969
source quote (p.9)
“The test results for pulmonary lobe segmentation showed that the average Dice Coefficient was 0.966 (95%CI: 0.962 to 0.969).”

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/K240554