NeuroQuant

K241098

CorTechs Labs, Inc. · cleared 2024-08-22 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“NeuroQuant is a fully automated MR imaging post-processing software medical device that provides automatic labeling, visualization, and volumetric quantification of brain structures and lesions from a set of MR images and returns segmented images and morphometric reports.”
Algorithmdynamic probabilistic neuroanatomical atlas, with age and gender specificity, based on the MR image intensity and static deep-learning technologies; three deep-learning models were included: Brain Segmentation; FLAIR Lesion Segmentation; and MCH Segmentation.
source quote (p.6)
“Automatic segmentation and quantification of brain structures and lesions using a dynamic probabilistic neuroanatomical atlas, with age and gender specificity, based on the MR image intensity and static deep-learning technologies”
Adaptive (vs locked)No
source quote (p.6)
“static deep-learning technologies”
PCCPNo
Cybersecurity addressedNo

Validation studies (3)

Retrospective clinical

n=30 patients · 16 site(s)

endpoints: Dice Similarity Coefficient (DSC)

Retrospective clinical

n=63 patients · 22 site(s)

endpoints: Dice Similarity Coefficient (DSC)

Retrospective clinical

n=117 patients · 68 site(s)

endpoints: F1 Score

Reported performance (3 observations)

diceas written: “Dice Similarity Coefficient (DSC)”stated without value
source quote (p.8)
“The NeuroQuant Brain Segmentation Model was evaluated using the Dice Similarity Coefficient (DSC) as the primary performance metric. The model's performance was assessed against the predicate device and meets the acceptance criteria for accuracy and reproducibility.”
diceas written: “Dice Similarity Coefficient (DSC)”0.7
source quote (p.8)
“achieving a mean Dice Similarity Coefficient (DSC) of 0.70 with a standard deviation of 0.14, surpassing the acceptance criteria in comparison to the predicate device of mean DSC ">= 0.50 and standard deviation <= 0.18.”
f1as written: “F1 Score”0.6
source quote (p.8)
“The MCH Detection Model in NeuroQuant exhibited robust performance, achieving a median F1 Score of 0.60, which exceeded the acceptance criteria of ">= 0.51.”

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/K241098