Contour+ (MVision AI Segmentation)

K241490

MVision AI Oy · cleared 2024-10-18 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Contour+ (MVision Al Segmentation) is a software-only medical device (software system) that can be used to accelerate region of interest (ROI) delineation in radiotherapy treatment planning by automatic contouring of predefined ROIs and the creation of segmentation templates on CT and MR images.”
Algorithmmachine learning using deep artificial neural networks
source quote (p.6)
“Automatic contouring of predefined ROIs is performed by pre-trained, locked, and static models that are based on machine learning using deep artificial neural networks.”
Adaptive (vs locked)No
source quote (p.11)
“In both software versions, automatic contouring of images (segmentation of anatomical regions of interest) is performed by pre-trained, locked, and static models that are based on machine learning using the same deep artificial neural networks.”
PCCPNo
Cybersecurity addressedYes
source quote (p.11)
“Verification and validation testing of this software release was also conducted as per FDA's Guidance for the "Content of Premarket Submissions for Device Software Functions (2023)," including compliance with recognized consensus standards (e.g., IEC 62304, IEC 62366-1, ISO 14971, DICOM) and FDA guidance for “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (2023),” and “Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices (2017).””

Validation studies (1)

Retrospective clinical

sample size not stated

endpoints: DSC (Dice Score); S-DSC@2mm (Surface-Dice Score)

standards: IEC 62304, IEC 62366-1, ISO 14971, DICOM

Reported performance (2 observations)

diceas written: “DSC (Dice Score)”stated without value
source quote (p.11)
“The performance of both CT and MR automatic segmentation models was evaluated by comparing the produced auto-segmentations to ground truth segmentations and calculating similarity scores DSC (Dice Score) and S-DSC@2mm (Surface-Dice Score) for all regions of interest (ROI).”
diceas written: “S-DSC@2mm (Surface-Dice Score)”stated without value
source quote (p.11)
“The performance of both CT and MR automatic segmentation models was evaluated by comparing the produced auto-segmentations to ground truth segmentations and calculating similarity scores DSC (Dice Score) and S-DSC@2mm (Surface-Dice Score) for all regions of interest (ROI).”

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/K241490