InferOperate Suite

K250237

Beijing Infervision Healthcare Medical Technology Co., Ltd. · cleared 2025-09-15 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
InferOperate Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, reporting, and treatment planning for patients, including both preoperative surgical planning and intraoperative image display. InferOperate Suite accepts DICOM compliant medical images acquired from a variety of imaging devices. InferOperate Suite utilizes machine learning-based algorithms for adult patients undergoing CT chest, abdominal, or pelvic scans.
Algorithmmachine learning and other computer vision algorithms
source quote (p.6)
The processing may include the generation of preliminary segmentations of anatomy using software that employs machine learning and other computer vision algorithms, as well as interactive segmentation tools, etc.
Adaptive (vs locked)No
source quote (p.12)
In accordance with the PCCP, the modified algorithms will be adequately trained, tuned, tested, and locked before release.
PCCPYes
source quote (p.2)
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act.
Cybersecurity addressedYes
source quote (p.10)
Additionally, the software validation activities were performed in accordance with IEC 62304 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, "Content of Premarket Submissions for Device Software Functions” and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions." ... • Cybersecurity Testing and Analysis

Validation studies (1)

Retrospective clinical

n=188 cases

endpoints: Dice coefficient (DSC); 95% Hausdorff Distance (HD95) (mm)

standards: IEC 62304 - Medical device software – Software life cycle processes, FDA Guidance documents, "Content of Premarket Submissions for Device Software Functions”, FDA Guidance documents, "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions."

Reported performance (1 observation)

diceas written: “Dice coefficient (Bronchus)0.87CI 0.85-0.88
source quote (p.10)
Bronchus 70 0.87 0.85-0.88 0.79 2.33 2.07-2.59 3.5

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
3
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/K250237