ScanDiags Ortho L-Spine MR-Q

K242607

ScanDiags AG · cleared 2025-02-21 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“ScanDiags Ortho L-Spine MR-Q software is a software as a medical device (SaMD)”
AlgorithmSupervised Deep Convolutional Neural Network (DCNN), both for classification (image-to-class) to and segmentation (image-to-image) model architectures. Combines deep learning, image analysis, as well as regression-based machine learning methods.
source quote (p.6)
“The semi-automatic segmentations are based on deep convolutional neural networks (DCNN) which are developed by applying well-established supervised deep learning methods on unstructured MRI scans (DICOM image format). ScanDiags Ortho L-Spine MR-Q combines deep learning, image analysis, as well as regression-based machine learning methods. Supervised Deep Convolutional Neural Network (DCNN), both for classification (image-to-class) to and segmentation (image-to-image) model architectures”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
“The ScanDiags Ortho L-Spine MR-Q Software is HIPAA Compliant by preventing unauthorized access (only authenticated and authorized users can access DICOM data and preliminary Measurement Results in built-in ScanDiags DICOM Viewer), encrypting data in transfer (both DICOM-TLS, SSL certificates in use). The DICOM data transferred to and processed by ScanDiags Ortho L-Spine MR-Q Software only temporarily stores the data until it is either approved or rejected by the intended user. The vulnerability assessment and penetration testing demonstrates satisfactory security performance.”

Validation studies (1)

Retrospective clinical

n=100 patients · 2 site(s)

endpoints: segmentations within acceptable measurement range; Dice score above a set threshold; accuracy; overall functional performance; Intraclass Correlation Coefficient (ICC); Dice Similarity Coefficient (DSC); Mean Absolute Error (MAE)

Reported performance (4 observations)

agreement_kappaas written: “Vertebra Area ICC”0.95CI [0.94 - 0.96]
source quote (p.10)
“Area 0.95 [0.94 - 0.96]”
agreement_kappaas written: “Vertebra Anterior Height ICC”0.85CI [0.30 - 0.94]
source quote (p.10)
“Anterior Height 0.85 [0.30 -0.94]”
diceas written: “Vertebra DICE”0.95CI [0.95 - 0.96]
source quote (p.10)
“0.95 [0.95 -0.96]”
diceas written: “Neuroforamen DICE”0.86CI [0.85 - 0.86]
source quote (p.10)
“0.86 [0.85 -0.86]”

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/K242607