InferRead Lung CT.AI

K192880

Beijing Infervision Technology Co.,Ltd. · cleared 2020-07-02 · product code OEB · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.5)

“InferRead Lung CT.AI uses the Browser/Server architecture, and is provided as Software as a Service (SaaS) via a URL. The system integrates algorithm logic and database in the same server to ensure the simplicity of the system and the convenience of system maintenance. The server is able to accept chest CT images from a PACS system, Radiological Information System (RIS system) or directly from a CT scanner, analyze the images and provide output annotations regarding lung nodules. Users are then able to use an existing PACS system to view the annotations on their workstations. Dedicated servers can be located at hospitals and are directly connected to the hospital networks. The software consists of 4 modules which are Image reception (Docking Toolbox), Image predictive processing (DLServer), Image storage (RePACS) and Image display (NeoViewer).”

AlgorithmImage predictive processing (DLServer)

source quote (p.5)

“The software consists of 4 modules which are Image reception (Docking Toolbox), Image predictive processing (DLServer), Image storage (RePACS) and Image display (NeoViewer).”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

Validation studies (3)

Bench

sample size not stated

standards: General Principles of Software Validation; Final Guidance for Industry and FDA Staff (January 11, 2002), ISO 14971:2007

Standalone

sample size not stated

Reader study (MRMC)

n=249 scans

endpoints: area under the curve (AUC) of the localization receiver operating characteristic (LROC) response; radiologists' interpretation time

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

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).