Medihub Prostate

K233196

JLK Inc. · cleared 2024-06-21 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“MEDIHUB PROSTATE is an image processing software package for multi-parametric prostate MR image analysis.”
AlgorithmAI-based algorithm; deep learning technique
source quote (p.5)
“In semi-automatic mode, our device employs an Al-based algorithm to initially outline the prostate volume, and then it requires the user to edit, review and approve. Additionally, the device calculates the total prostate volume. However, users are responsible for performing all other image annotations and measurements manually. This implies that the final decision should be confirmed by the user, and the user should not rely solely on the device's analysis. MEDIHUB PROSTATE was developed by applying a deep learning technique on T2 prostate MRI.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedNo

Validation studies (3)

Bench

n=235 cases

endpoints: Dice coefficient; Hausdorff Distance

standards: ISO 14971, IEC 62304, IEC 62366

Standalone

n=114 images · 1 site(s)

endpoints: mean Dice coefficient; Hausdorff distance

standards: ISO 14971, IEC 62304, IEC 62366

Reader study (MRMC)

n=73 cases · 1 site(s)

endpoints: Dice coefficients

standards: ISO 14971, IEC 62304, IEC 62366

Reported performance (6 observations)

diceas written: “overall Dice coefficient”0.928CI [0.925, 0.931]
source quote (p.9)
“The clinical testing results demonstrated that the overall Dice coefficient and Hausdorff distance were 0.928 and 2.171, respectively, with the 95% confidence intervals for these measurements being [0.925, 0.931] for the Dice coefficient and [1.097, 3.245] for the Hausdorff distance.”
diceas written: “Dice Coefficient (White)”0.929CI [0.925, 0.933]
source quote (p.10)
“95% confidence interval of the dice coefficient and Hausdorff distance calculated by comparing ground truth and the result of algorithms by race”
diceas written: “Dice Coefficient (African American)”0.925CI [0.902, 0.949]
source quote (p.10)
“95% confidence interval of the dice coefficient and Hausdorff distance calculated by comparing ground truth and the result of algorithms by race”
diceas written: “Dice Coefficient (Asian)”0.917CI [nan, nan]
source quote (p.10)
“95% confidence interval of the dice coefficient and Hausdorff distance calculated by comparing ground truth and the result of algorithms by race”
diceas written: “Dice Coefficient (Age < 60)”0.938CI [0.925, 0.950]
source quote (p.10)
“95% confidence interval of the dice coefficient and Hausdorff distance calculated by comparing ground truth and the result of algorithms by age”
diceas written: “Dice Coefficient (Age >= 60)”0.927CI [0.923, 0.931]
source quote (p.10)
“95% confidence interval of the dice coefficient and Hausdorff distance calculated by comparing ground truth and the result of algorithms by age”

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/K233196