RUS
K233457Hutom Inc. · cleared 2024-07-12 · product code QIH · Radiology
Premarket evidence — what FDA accepted
Device typesamd
source quote (p.5)
“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.”
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)
“Additionally, the software validation activities were performed in accordance with IEC 62304:2006/Amd 1: 2015- Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices.””
Validation studies (1)
Retrospective clinical
n=60 cases
endpoints: Segmentation Accuracy; pneumoperitoneum; Length Measurement
standards: IEC 62304:2006/Amd 1: 2015- Medical device software – Software life cycle processes
Reported performance (2 observations)
diceas written: “Organ DSC”0.927
source quote (p.13)
“The performance of the machine learning models, characterized by the Dice coefficient Scores (DSC) and Mean Absolute Error (MEA), was as follows: Organ 0.927 DSC”
diceas written: “Vessel DSC”0.92
source quote (p.13)
“The performance of the machine learning models, characterized by the Dice coefficient Scores (DSC) and Mean Absolute Error (MEA), was as follows: Organ 0.927 DSC; Vessel 0.920 DSC”
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).