Seg Pro V3 (RT-300)
K251306Ever Fortune.Ai, Co., Ltd. · cleared 2026-01-28 · product code QKB · Radiology
Premarket evidence — what FDA accepted
Device typesamd
source quote (p.6)
“The proposed device, Seg Pro V3, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate (segment/contour) organs-at-risk (OARs) on DICOM images.”
Algorithmdeep-learning algorithms
source quote (p.6)
“The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems.”
Adaptive (vs locked)FDA source did not state this
PCCPYes
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).”
Cybersecurity addressedYes
source quote (p.11)
“Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”(2005) and the recently published “Content of Premarket submissions for Devices Software Functions (11-04-2021), and “Content of Premarket Submission for Management of Cybersecurity in Medical Devices.””
Validation studies (2)
Reader study (MRMC)
sample size not stated
Standalone
n=175 cases
endpoints: mean Dice Similarity Coefficient (DSC); 95% Hausdorff Distance (HD)
standards: IEC 62304:2006/A1:2016, ISO 14971:2019
Reported performance (4 observations)
diceas written: “overall mean DSC”0.85
source quote (p.11)
“The overall performance demonstrated a mean DSC of 0.85.”
diceas written: “mean DSC for 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 DSC for 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 DSC for 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). 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).