ScanDiags Ortho L-Spine MR-Q

by ScanDiags AG  · Based in Switzerland → — Pioneering the AI frontier, ScanDiags amplifies the precision and efficiency of MRI analysis.
Neurosurgery Orthopedics Radiology

Contact for pricing
Regulatory Status Disclosed

Overview

ScanDiags Ortho L-Spine MR-Q is an AI-powered Software as a Medical Device (SaMD) designed for the visualization and quantification of lumbar spine anatomical structures from standard DICOM MRI images. The software employs deep learning, image analysis, and regression-based machine learning methods to perform semi-automatic segmentation of structures such as vertebral bodies, intervertebral discs, neuroforamina, and thecal sacs. It provides distance and area measurement outputs, which are user-modifiable. The tool assists radiologists by generating PDF reports that include images and measurements, which are reviewed and approved by the radiologist before being sent to the clinician’s PACS system. ScanDiags Ortho L-Spine MR-Q is an adjunct tool, not intended to replace a radiologist’s review or clinical judgment, and does not detect, diagnose, or identify abnormalities. It is compatible with all MRI hardware platforms and integrates seamlessly into existing radiology operations, uploading previously generated DICOM data files for analysis.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Combines deep learning, image analysis, and regression-based machine learning methods
  • Visualization and quantification of lumbar spine anatomical structures
  • Semi-automatic segmentation of vertebral bodies, intervertebral discs, neuroforamina, thecal sacs
  • Distance and area measurement outputs
  • User-modifiable segmentations and distance measurements
  • Generates PDF reports with images and measurements
  • Integrates with PACS systems for report storage
  • Includes ScanDiags DICOM Viewer for review and corrections
  • Manufacturer-, PACS-, and parameter-independent
  • Performs at or above human precision

Use Cases

  • Augmented diagnosis from musculoskeletal MRI
  • Standardized diagnostic reports in an automated process
  • Unburdening clinicians and increasing radiology workflow capacity
  • Assisting radiologists in reviewing and interpreting lumbar spine MRI studies
  • Providing quantitative spine measurements

What Physicians Need to Know

DICOM Support & Standards
ScanDiags Ortho L-Spine MR-Q processes standard lumbar spine MRI images in DICOM (Digital Imaging and Communications in Medicine) format. It includes a dedicated 'ScanDiags DICOM Viewer' for visualization, review, and applying corrections to measurement results. The software is HIPAA Compliant and ensures data privacy through encryption during transfer using DICOM-TLS and SSL certificates.
Reading Room Workflow Impact
ScanDiags Ortho L-Spine MR-Q is an adjunct tool designed to assist in visualizing and documenting area and distance measurements of lumbar spine anatomical structures. It is not intended to replace a radiologist's review, clinical judgment, or to detect, diagnose, or identify abnormalities. The semi-automatic segmentations are user-modifiable, and radiologists must review and approve the software-generated measurements and draft report content. This can help reduce radiologist workload by automating time-consuming measurements and aims to improve efficiency and consistency in clinical imaging workflows.
AI Model Architecture (deep learning approach)
The semi-automatic segmentations are based on deep convolutional neural networks (DCNNs), developed by applying well-established supervised deep learning methods on unstructured MRI scans. The tool combines deep learning, image analysis, and regression-based machine learning methods.
Processing Speed (per study)
Specific processing speed per study for ScanDiags Ortho L-Spine MR-Q is not explicitly detailed in the provided information.
FDA Clearance Pathway (510k/De Novo)
ScanDiags Ortho L-Spine MR-Q received 510(k) clearance (K242607) from the U.S. Food and Drug Administration (FDA) in February 2025. It is classified as a 'software as a medical device (SaMD)' and was determined to be substantially equivalent to legally marketed devices.
Supported Modalities
The software is specifically designed for and only supports DICOM images of MRI acquired from lumbar spine exams. It is intended for patients aged 22 and above and does not support images from pregnant patients, those undergoing MRI with contrast media, or patients with post-operational complications or infections.
Sensitivity & Specificity Data
Specific sensitivity and specificity data for ScanDiags Ortho L-Spine MR-Q are not explicitly provided in the available information.
RSNA/ACR Validation
Direct RSNA/ACR validation for ScanDiags Ortho L-Spine MR-Q is not explicitly mentioned in the provided information.
Physician Tip

Utilize ScanDiags Ortho L-Spine MR-Q as a quantitative aid for lumbar spine MRI analysis, focusing on its ability to provide objective measurements and segmentations of vertebral bodies, intervertebral discs, neuroforamina, and thecal sacs. Remember that the tool is supplementary; always exercise clinical judgment and perform a thorough review of the original MRI study. Leverage the user-modifiable segmentations to refine results and ensure accuracy before generating the final PDF report. The automated measurements can significantly streamline report generation, allowing more focus on complex diagnostic interpretations. Be aware of the specified input criteria, such as patient age and exclusion of contrast studies or specific patient conditions, to ensure appropriate use of the software.

ScanDiags Ortho L-Spine MR-Q integrates into the existing radiology workflow by accepting pre-acquired DICOM MRI images. The primary output for integration with hospital systems is a PDF report, which is sent to the clinician's PACS system and stored with the patient's MRI study. This method avoids direct real-time interfacing with MR equipment or direct DICOM output back to PACS, simplifying deployment but requiring a workflow that accommodates PDF report ingestion into PACS. Ensure your PACS can effectively store and display these PDF reports alongside the original imaging studies for comprehensive patient records.

Details

Category Radiology & Imaging AI
Pricing Contact for pricing
DeploymentSoftware as a Medical Device (SaMD) with PACS integration
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

ScanDiags Ortho L-Spine MR-Q software received 510(k) clearance from the FDA on February 21, 2025, as a Class II medical device (Product Code QIH) for visualization and quantification of lumbar spine anatomical structures from standard MRI images.

Integrations
EHR Not specified
Specialties Neurosurgery, Orthopedics, Radiology

What the Web Says

ScanDiags Ortho L-Spine MR-Q is an AI-powered software tool designed to assist physicians in analyzing lumbar spine MRI images. It provides quantitative measurements of anatomical structures like vertebral bodies, intervertebral discs, neuroforamina, and thecal sacs using deep learning algorithms for semi-automatic segmentation. The software aims to save time by automating manual measurement tasks and generating PDF reports, but it is intended as an adjunct tool and not a replacement for a radiologist's expert interpretation or clinical judgment.

Overall: Mixed

Strengths

  • Automates tedious manual segmentation and measurement tasks, saving time for clinicians.
  • Utilizes deep convolutional neural networks (DCNN) and machine learning for semi-automatic segmentation and measurements, offering high accuracy.
  • Provides quantitative spine measurements from previously acquired DICOM lumbar spine MR images.
  • Allows users to review and modify segmentations and measurements before generating PDF reports.
  • Independent of MRI acquisition equipment and vendor.
  • Validated in studies showing high intraclass correlation coefficients (ICCs) for measurements and Dice similarity coefficients for segmentation accuracy.

Limitations

  • Not intended to replace a radiologist's review, clinical judgment, or to detect/diagnose abnormalities.
  • Radiologists must not use the generated output as a primary interpretation.
  • Only for use with DICOM lumbar spine MR images of patients aged 22 and above; not for pregnant patients, those with contrast media, post-operational complications, or infections.
  • No direct reviews or opinions from physicians, healthcare IT, tech reviewers, Reddit, G2, or Capterra were found for this specific product.
  • General concerns exist regarding the potential for misdiagnosis in lumbar spine MRIs, even with expert interpretation, highlighting the need for careful review of AI-generated results.

Based on reviews from: FDA Radiology AI Device - X-ray Interpreter, ScanDiags | Radiology AI Companies - X-ray Interpreter, accessdata.fda.gov, Microsoft Marketplace, Reddit (r/ChronicPain), Reddit (r/backpain), Reddit (r/orthopaedics), G2, MDView, Capterra, Emeralgo

Last updated: 2026-07-18

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Press & Coverage

X-ray Interpreter
ScanDiags Ortho L-Spine MR-Q | FDA Radiology AI Device - X-ray Interpreter
ScanDiags Ortho L-Spine MR-Q is an AI-powered software tool that received FDA approval on February 21, 2025, designed to provide quantitative measurements of spinal anatomical structures from lumbar spine MRI images. It uses deep learning for semi-automatic segmentation and measurements, aiming to improve accuracy and save time for clinicians.
2025-02
Innolitics
K242607 ScanDiags Ortho L-Spine MR-Q Cleared Traditional 2025-02-21
This entry from Innolitics' FDA Product Code database confirms the traditional clearance of ScanDiags Ortho L-Spine MR-Q on February 21, 2025, with the product code QIH for automated radiological image processing software.
2025-02
accessdata.fda.gov
ScanDiags AG Stefan Voser COO, Product Manager Zwicky-Platz 1 Wallisellen, 8304 Switzerland Re: K242607 Trade/Device Name: Scan - accessdata.fda.gov
The FDA clearance document details that ScanDiags Ortho L-Spine MR-Q is a software as a medical device (SaMD) that uses deep learning, image analysis, and machine learning for quantitative measurements of lumbar spine structures from MRI images. It's intended for use by hospitals and medical institutions for patients aged 22 and above, and the generated reports are reviewed and approved by radiologists.
2025-02
X-ray Interpreter
ScanDiags | Radiology AI Companies - X-ray Interpreter
ScanDiags is listed among Radiology AI Companies, highlighting their Ortho L-Spine MR-Q as an AI-powered software for quantitative measurements of lumbar spine MRI images. The tool aims to enhance assessment accuracy and save time for clinicians without replacing expert interpretation.
unknown
Innolitics
Definitive Guide to AI/ML SaMD Ground Truthing - Innolitics
This article discusses ground truthing for AI/ML SaMD and references ScanDiags Ortho L-Spine MR-Q (K242607) as an example where consensus ground truth for anatomic structure segmentation was determined by pixel-based majority opinion among three radiologists.
2025-09
accessdata.fda.gov
TPLC - Total Product Life Cycle - accessdata.fda.gov
The FDA's Total Product Life Cycle (TPLC) database entry for ScanDiags Ortho L-Spine MR-Q (K242607) categorizes it under 'Medical image management and processing' with a regulation description.
2026-06
MEDICREA INTERNATIONAL radiology AI
UNiD Spine Analyzer | MEDICREA INTERNATIONAL radiology AI ...
ScanDiags Ortho L-Spine MR-Q is listed as an image analysis/processing tool for the spine using MRI, with a decision date of February 21, 2025, focusing on MSK and bone anatomy.
2025-02
HealthAidb
healthcare Software Solutions u2014 Category - HealthAidb u2014 Software
ScanDiags Ortho L-Spine MR-Q is categorized under healthcare software solutions, specifically imaging software and radiology, indicating its role in AI-driven clinical documentation integrity and medical device regulatory compliance.
unknown

Videos

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Frequently Asked Questions

ScanDiags Ortho L-Spine MR-Q integrates by allowing the upload of pre-existing DICOM images of lumbar spine MRI exams for analysis. The software then provides semi-automatic segmentations and measurements, which radiologists can review, modify, and approve using the ScanDiags DICOM Viewer before a PDF report is generated and sent to the PACS system.
ScanDiags Ortho L-Spine MR-Q has received FDA 510(k) clearance (K242607) as a software as a medical device (SaMD). It is designed to be HIPAA compliant, preventing unauthorized access through authenticated users and encrypting data during transfer using DICOM-TLS and SSL certificates.
The software is intended for patients aged 22 and above and does not support DICOM images from pregnant patients, those who underwent MRI with contrast media, or individuals with post-operational complications or infections. It is an adjunct tool, not intended to replace a radiologist's review or clinical judgment, and does not detect, diagnose, or identify abnormalities.
ScanDiags Ortho L-Spine MR-Q utilizes deep learning, image analysis, and regression-based machine learning for semi-automatic segmentation and quantification of lumbar spine anatomical structures. While specific direct comparisons are not detailed, other FDA-cleared AI tools for spine MRI, such as MSKai and RAI, also offer automated segmentation and disc measurements aimed at reducing reading times.
Specific pricing details for ScanDiags Ortho L-Spine MR-Q are not publicly available in the provided information. Typically, medical AI software solutions are offered through various models, which may include subscription-based licensing, per-study fees, or enterprise-level agreements. Physicians interested in implementation should contact ScanDiags directly for detailed pricing information.
The device underwent verification and validation testing to demonstrate substantial equivalence to a predicate device. A retrospective, multicenter study was conducted to evaluate the machine learning model's capability in calculating quantified values, successfully passing primary intraclass correlation coefficient (ICC) acceptance criteria across all structures.

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Suggest an Edit → | Last Verified: 2026-04-19 | First Added: 2026-04-19
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