VUNO Med-DeepBrain
K231398VUNO Inc. · cleared 2023-10-04 · product code QIH · Radiology
Premarket evidence — what FDA accepted
Device typesamd
source quote (p.4)
“The VUNO Med-DeepBrain is intended for automatic labeling, quantification and visualization of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying segmentable brain structures identified on MR images.”
Algorithmmachine learning technique based on deep learning
source quote (p.7)
“The algorithm used in both software is based on the machine learning technique in which the device learns the characteristics of brain MR images from a large dataset. However, there are several differences in specific learning techniques based on machine learning. The predicate used a multi-atlas segmentation, while the subject device is based on deep learning.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.9)
“VUNO Med-DeepBrain is a cyber device and the risks associated with cybersecurity are identified and addressed. The device meets the requirement under the FDA Guidance, “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff (October 2, 2014)".”
Validation studies (1)
Bench
sample size not stated
endpoints: Segmentation accuracy (Dice Similarity Coefficient); average relative volume errors; average absolute volume errors; Test-retest reproducibility (intraclass correlation coefficient)
Reported performance (3 observations)
diceas written: “Dice Similarity Coefficient (DSC) score”stated without value
source quote (p.9)
“The acceptance criteria are an average DSC score of 0.80 in brain regions and White Matter Hyperintensities(WMH) regions as referred to in the literature. Whole brain regions including cortical and subcortical as well as WMH regions exceeded the criteria.”
agreement_kappaas written: “Intraclass correlation coefficient (ICC) for brain structures”stated without value
source quote (p.10)
“Test-retest reproducibility is also measured by the intraclass correlation coefficient. The acceptance criteria are set to 0.965 for brain structures and 0.988 for WMH. The result exceeded criteria which mean excellent reliability.”
agreement_kappaas written: “Intraclass correlation coefficient (ICC) for WMH”stated without value
source quote (p.10)
“Test-retest reproducibility is also measured by the intraclass correlation coefficient. The acceptance criteria are set to 0.965 for brain structures and 0.988 for WMH. The result exceeded criteria which mean excellent reliability.”
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).