Seg Pro V3 (RT-300)

K251306

Ever Fortune.Ai, Co., Ltd. · cleared 2026-01-28 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Seg Pro V3 is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk on DICOM images.”
Algorithmdeep-learning algorithms
source quote (p.4)
“The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems.”
Adaptive (vs locked)Yes
source quote (p.16)
“This Predetermined Change Control Plan (PCCP) includes a planned modification to the Seg Pro V3 system involving the re-training of the deep learning model using newly acquired clinical data to improve performance in auto-contouring organs at risk (OARs).”
PCCPYes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedYes
source quote (p.11)
“and “Content of Premarket Submission for Management of Cybersecurity in Medical Devices.””

Validation studies (1)

Standalone

n=175 cases

endpoints: statistical superiority of the mean Dice Similarity Coefficient (DSC) over the predefined thresholds of 0.80, 0.65, and 0.50 for large-, medium-, and small-volume structures, respectively.; the lower bound of the 95% confidence interval (CI) for the mean DSC exceeding the corresponding reference standard threshold.

standards: IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”(2005), Content of Premarket submissions for Devices Software Functions (11-04-2021), Content of Premarket Submission for Management of Cybersecurity in Medical Devices., ISO 14971:2019 Medical devices — Application of risk management to medical devices.

Reported performance (4 observations)

diceas written: “Mean Dice Similarity Coefficient (DSC) (overall)”0.85
source quote (p.11)
“The overall performance demonstrated a mean DSC of 0.85.”
diceas written: “Mean Dice Similarity Coefficient (DSC) (large-volume structures)”0.9
source quote (p.11)
“The observed mean DSC values of 0.90, 0.86, and 0.73 for large-, medium-, and small-volume structures, respectively.”
diceas written: “Mean Dice Similarity Coefficient (DSC) (medium-volume structures)”0.86
source quote (p.11)
“The observed mean DSC values of 0.90, 0.86, and 0.73 for large-, medium-, and small-volume structures, respectively.”
diceas written: “Mean Dice Similarity Coefficient (DSC) (small-volume structures)”0.73
source quote (p.11)
“The observed mean DSC values of 0.90, 0.86, and 0.73 for large-, medium-, and small-volume structures, respectively.”

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
0
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/K251306