Surgical Reality Viewer

K252091

Medicalvr B.V. · cleared 2026-01-29 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Surgical Reality Viewer is a medical imaging visualization software intended to assist trained healthcare professionals with preoperative and intraoperative visualizations, by displaying 2D and 3D renderings of DICOM compliant patient images and normal anatomic segmentations derived from patient images as well as functions for manipulation of segmentations and 3D models. The machine learning algorithms in use by Surgical Reality Viewer are intended for use on adult patients aged 22 years and over.”
Algorithmmultiple machine learning algorithms which support the segmentation of anatomical structures within CT chest images for 3D visualization
source quote (p.9)
“Surgical Reality Viewer includes multiple machine learning algorithms which support the segmentation of anatomical structures within CT chest images for 3D visualization.”
Adaptive (vs locked)No
source quote (p.9)
“Each of the algorithms has been trained and tuned on curated datasets representative of the intended patient population.”
PCCPNo
Cybersecurity addressedYes
source quote (p.9)
“Surgical Reality Viewer is developed in line with the IEC 62304/2006/Amd 1: 2015 standard on 'Medical device software - Software life cycle processes' in addition to application of the supporting FDA guidance documents on premarket submissions for software, the IEC 81001-5-1 on 'Health software and health IT systems safety, effectiveness and security - Part 5-1: Security – Activities in the product life cycle' and the FDA guidance regarding cybersecurity on quality system considerations and content of premarket submissions.”

Validation studies (1)

Retrospective clinical

n=102 images

endpoints: Sørensen-Dice coefficient (DSC) for Lobe segmentation; Sørensen-Dice coefficient (DSC) for Vessel segmentation; Sørensen-Dice coefficient (DSC) for Airway segmentation; Sørensen-Dice coefficient (DSC) for Aorta segmentation; Sørensen-Dice coefficient (DSC) for Pulmonary segmentation

standards: IEC 62304/2006/Amd 1: 2015, IEC 81001-5-1

Reported performance (5 observations)

diceas written: “Lobe segmentation average DICE”0.97
source quote (p.10)
“Lobe segmentation accuracy resulted in an average DICE of 0.97 (LUL: 0.98, LLL: 0.98, RUL: 0.98, RLL: 0.98, RML: 0.96)”
diceas written: “Vessel segmentation average DICE”0.84
source quote (p.10)
“Vessel segmentation accuracy resulted in an average DICE of 0.84 (Artery: 0.84, Vein: 0.83)”
diceas written: “Airway segmentation average DICE”0.96
source quote (p.10)
“Airway segmentation accuracy resulted in an average DICE of 0.96”
diceas written: “Aorta segmentation average DICE”0.96
source quote (p.10)
“Aorta segmentation resulted in an accuracy of 0.96”
diceas written: “Pulmonary segmentation average DICE”0.85
source quote (p.10)
“Pulmonary segmentation accuracy resulted in an average DICE 0.85 (left segments: 0.85, right segments: 0.85)”

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/K252091