RUS

K233457

Hutom Inc. · cleared 2024-07-12 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“RUS 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. RUS accepts DICOM compliant medical images acquired from iodine contrast-enhanced abdomen CT.”
AlgorithmThree machine learning models are included in RUS. (Organ: CADD U-NET, Vessel: 3D U-NET, Pneumoperitoneum: Linear regression).
source quote (p.12)
“Three machine learning models are included in RUS. (Organ: CADD U-NET, Vessel: 3D U-NET, Pneumoperitoneum: Linear regression).”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.12)
“Content of Premarket Submission for Management of Cybersecurity in Medical Devices.””

Validation studies (2)

Retrospective clinical

n=60 images

endpoints: Segmentation Accuracy; Pneumoperitoneum

standards: IEC 62304:2006/Amd 1: 2015

Bench

sample size not stated

endpoints: accuracy of length measurement

Reported performance (3 observations)

diceas written: “Organ DSC”0.927
source quote (p.13)
“Organ 0.927 DSC”
diceas written: “Vessel DSC”0.92
source quote (p.13)
“Vessel 0.920 DSC”
accuracyas written: “Length Measurement Accuracy”stated without valueCI +/- 10%
source quote (p.13)
“The measurements produced by RUS were verified to be accurate within a mean difference of +/- 10%.”

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); 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/K233457