Alzevita

K252670

TOPIA MEDTECH LIMITED · cleared 2025-12-19 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Alzevita is a cloud-based, Al-powered medical image processing software as a medical device intended to assist neurologists and radiologists with expertise in the analysis of 3D brain MRI scans.”
AlgorithmAlzevita is a deep learning algorithm-based device. This algorithm is developed by training the Deep Learning based 3D U-Net++ model with the help of the training data.
source quote (p.8)
“Alzevita is a deep learning algorithm-based device. This algorithm is developed by training the Deep Learning based 3D U-Net++ model with the help of the training data.”
Adaptive (vs locked)No
source quote (p.6)
“The underlying algorithm used in Alzevita is locked, meaning it does not modify its behavior at runtime or adapt to new inputs.”
PCCPNo
source quote (p.6)
“Any future modifications to the algorithm including performance updates or model re-training will be submitted to the FDA for review and clearance prior to deployment, in compliance with FDA regulatory requirements and applicable guidance for Al/ML-based SaMD.”
Cybersecurity addressedYes
source quote (p.6)
“The software operates through a secure web interface and is compatible with commonly used operating systems and browsers.”

Validation studies (1)

Retrospective clinical

n=298 patients

endpoints: Dice Score: ", "≥ 75%; Hausdorff Distance: ", "≤ 6.1 mm; Correlation Coefficient: ", "≥ 0.82; Relative Volume Difference: ", "≤ 24.6%; Bland-Altman Mean Difference (Total Hippocampus Volume): ", "≤ 1010 mm³

Reported performance (1 observation)

diceas written: “Dice Score”0.86CI (0.85, 0.86)
source quote (p.11)
“The average dice coefficient and Hausdorff distance is found to be 0.86 and 1.51 mm respectively. The following table shows the 95% confidence interval for both. ... Dice 0.75 (0.85, 0.86) Pass”

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/K252670