Contour+ (MVision AI Segmentation)
K241490MVision AI Oy · cleared 2024-10-18 · product code QKB · Radiology
Premarket evidence — what FDA accepted
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.”
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.”
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.”
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
n=0 patients
endpoints: DSC (Dice Score); S-DSC@2mm (Surface-Dice Score)
standards: IEC 62304, IEC 62366-1, ISO 14971, DICOM, FDA guidance for “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (2023),”, FDA guidance for “Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices (2017).”
Reported performance (2 observations)
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).”
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
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).