Surgical Reality Viewer
K252091Medicalvr 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.”
Algorithmmachine learning and computer vision algorithms
source quote (p.6)
“Surgical Reality Viewer can generate preliminary segmentations of normal anatomy on demand using machine learning and computer vision algorithms.”
Adaptive (vs locked)FDA source did not state this
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 (2)
Standalone
n=102 images
endpoints: Sørensen–Dice coefficient (DSC); segmentation accuracy
standards: IEC 62304/2006/Amd 1: 2015, IEC 81001-5-1
Reader study (MRMC)
sample size not stated
endpoints: suitability of the segmentations
Reported performance (5 observations)
accuracyas written: “Lobe segmentation accuracy (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)”
accuracyas written: “Vessel segmentation accuracy (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)”
accuracyas written: “Airway segmentation accuracy (average DICE)”0.96
source quote (p.10)
“Airway segmentation accuracy resulted in an average DICE of 0.96”
accuracyas written: “Aorta segmentation accuracy (average DICE)”0.96
source quote (p.10)
“Aorta segmentation resulted in an accuracy of 0.96”
accuracyas written: “Pulmonary segmentation accuracy (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). 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).