Bone VCAR (BVCAR)

by GE HealthCare  · Based in France → — Automated spine labeling with oblique and straightened reformat generation to improve ease of reading and reporting.
Neurosurgery Orthopedics Radiology

Regulatory Status Disclosed

Overview

Bone VCAR (BVCAR) is a deep learning-based software analysis package developed by GE Medical Systems SCS (now GE HealthCare) that assists in the analysis and visualization of CT Spine data. It is designed to enhance the efficiency and consistency of radiology workflows by providing automated spine identification and labeling. The tool generates a 3D trace to create oblique and straightened reformat views, as well as oblique views perpendicular to vertebral bodies and disc spaces. Clinicians can easily edit the centerline for accuracy, and the software is accessible for various exam types including trauma, oncology, dedicated spine, and general imaging. Bone VCAR can process multiple series and volumes, propagating vertebrae labeling across different acquisitions, and works on both full and limited spine studies. It boasts a high labeling accuracy (over 90%) based on a deep learning algorithm trained on global datasets. The application is a post-processing option compatible with the Advantage Workstation (AW) platform, CT Scanners, Cloud, or PACS stations.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automated spine labeling
  • Automated generation of 3D trace for oblique and straightened reformat views
  • Automated generation of oblique views perpendicular to vertebral bodies and disc spaces
  • Easy editing of the centerline for accurate placement
  • Accessible for various exam types: trauma, oncology, dedicated spine, general imaging
  • Propagation of vertebrae labeling across multiple series and volumes
  • Works on full spine acquisitions as well as limited acquisitions
  • Deep learning algorithm for >90% labeling accuracy
  • Fast spine labeling (under 5 seconds for volumes of 300mm or less)
  • Optimized display and quick access to tools for improved reading experience

Use Cases

  • Analysis and visualization of CT Spine data
  • Improving reading and reporting efficiency for spine imaging
  • Assessment of spinal health from CT images
  • Review of CT images for trauma cases involving the spine
  • Review of CT images for oncology cases involving the spine
  • General imaging review of the spine

What Physicians Need to Know

Supported Modalities
CT (specifically for spine imaging).
AI Model Architecture (deep learning approach)
Bone VCAR utilizes a deep learning algorithm. GE HealthCare is actively involved in developing and applying deep learning and AI across its imaging portfolio, including for image reconstruction and workflow optimization.
Processing Speed (per study)
Less than 5 seconds to label the spine for volumes of 300mm or less.
FDA Clearance Pathway (510k/De Novo)
510(k) cleared (K183204) on April 8, 2019.
Reading Room Workflow Impact
Automated spine labeling with oblique and straightened reformat generation improves ease of reading and reporting. It is designed to simplify the reading experience and improve reporting efficiency, assisting in the identification and review of vertebral bodies and discs. The software provides optimized display settings to facilitate fast image review and reporting of findings. AI-aided tools generally lead to shorter reading times for experienced radiologists.
DICOM Support & Standards
Bone VCAR is a post-processing application for the Advantage Workstation (AW) platform. GE HealthCare's Volume Viewer, which includes Bone VCAR, uses DICOM images and can save views in DICOM format (Secondary Capture or modality reformatted images). It can also save Structured Report objects to archive post-processing results.
PACS Integration Method
Bone VCAR is a post-processing application option for the Advantage Workstation (AW) platform, CT Scanner, Cloud, or PACS stations. GE HealthCare offers an Open PACS AI solution designed to seamlessly integrate third-party AI-based clinical applications into GE PACS radiology reading workflows. AI integration into PACS can enhance diagnostic accuracy and improve workflow efficiency.
Sensitivity & Specificity Data
The deep learning algorithm for automated spine identification and labeling has a >90% labeling accuracy. While not specific to Bone VCAR, a deep learning classifier for detecting primary bone lesions on radiographs achieved a sensitivity of 96.6% and specificity of 82.2% in a study.
RSNA/ACR Validation
GE HealthCare has partnered with the European Society of Radiology (ESR) to highlight AI applications in radiology. The RSNA and ACR, along with other societies, have issued joint statements on the development and use of AI tools in radiology, emphasizing responsible use and addressing key concerns for clinical practice.
Physician Tip

Bone VCAR is designed to streamline the review of CT spine images by automating vertebral labeling and generating optimized reformats. This can significantly reduce the time spent on routine tasks, allowing for more focus on complex cases. The high labeling accuracy and quick processing time can improve reporting efficiency and consistency. Be aware that while the tool is broadly applicable for various exam types including trauma and oncology, it is specifically for CT spine analysis. The ability to easily edit labels ensures flexibility in challenging cases. Consider how the automated generation of oblique and straightened reformats can enhance visualization and potentially aid in earlier detection or more precise characterization of spinal pathologies.

Bone VCAR functions as a post-processing application within the GE Advantage Workstation (AW) platform and can be deployed on CT scanners, cloud, or PACS stations. Its DICOM conformance allows for the saving of processed images and structured reports, facilitating integration with other DICOM-compliant applications and PACS systems. GE HealthCare's broader AI orchestration solutions and Open PACS AI solution aim to seamlessly integrate third-party AI applications into existing radiology workflows, suggesting a commitment to interoperability.

Details

Category Radiology & Imaging AI
Pricing Unknown
DeploymentSoftware analysis package, compatible with AW applications & platform.
TrainingUnknown
Compliance
BAA Available Yes AI-estimated
HIPAA Compliant Yes AI-estimated
FDA Status Cleared AI-estimated
Integrations
EHR Not specified
Specialties Neurosurgery, Orthopedics, Radiology

Support & Reliability

Training ProvidedUnknown

What the Web Says

Bone VCAR (BVCAR) by GE Healthcare is a software solution designed to aid in the analysis of bone scans. It is intended to help clinicians visualize and quantify bone lesions, particularly in the context of oncology.

Overall: Mixed

Strengths

  • Aids in the visualization of bone lesions
  • Quantification capabilities for lesion analysis
  • Part of the GE Healthcare imaging ecosystem
  • Designed to assist in oncology workflows

Limitations

  • Specific user reviews or detailed technical assessments are not readily available from general web searches.
  • Limited public information on direct physician or IT reviews outside of GE Healthcare's own materials.
  • Integration challenges with non-GE imaging systems are not explicitly addressed in publicly available information.
  • Potential learning curve for new users, though not explicitly stated in reviews.

Based on reviews from: GE Healthcare official website, Various medical imaging and oncology publications referencing GE Healthcare products

Last updated: 2026-10-30

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Press & Coverage

gehealthcare.com
Bone VCAR
Bone VCAR is a deep-learning based CT application for spine assessment that provides automated spine labeling with over 90% accuracy and generates oblique and straightened reformats in seconds to improve reading and reporting efficiency.
2024-03
auntminnie.com
GE focuses on artificial intelligence at ECR 2019
At ECR 2019, GE Healthcare showcased Bone VCAR, an AI algorithm trained on hundreds of CT spine cases to recognize different vertebrae with a single click, allowing labels to persist as radiologists window and level through images.
2019-02
fda.gov
510(k) Premarket Notification Submission - Bone VCAR
The FDA granted 510(k) clearance for Bone VCAR on April 8, 2019, as a post-processing application for CT images intended to support clinicians in reviewing spine images by providing tools to label the spine and optimize anatomical display.
2019-04
gehealthcare.com
Deep learning reconstruction for optimized bone assessment in zero echo time MR imaging of the knee
GE HealthCare's oZTEo, a zero echo time (ZTE) technique initially released in early 2020, enables MR bone imaging with CT-like contrast and has seen robust use in musculoskeletal applications.
2024-09
gehealthcare.com
Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction
AIR Recon DL, GE HealthCare's deep-learning-based reconstruction algorithm, is being retrained to be compatible with non-Cartesian, ZTE-based applications like oZTEo to significantly improve signal-to-noise ratio and potentially reduce scan times.
2024-12
gehealthcare.com
GE HealthCare unveils new AI-powered solutions at RSNA 2024
At RSNA 2024, GE HealthCare showcased Clarify DL, an AI-powered bone image reconstruction algorithm for nuclear medicine designed to enhance bone SPECT image quality for musculoskeletal and oncology applications.
2024-12
healthcarefinancenews.com
GE HealthCare announces FDA clearance for Aurora nuclear medicine system and Clarify DL
GE HealthCare received FDA clearance for its Aurora nuclear medicine system and Clarify DL, an AI-powered deep learning image reconstruction technology that improves bone SPECT image quality without increasing injected dose or scan time.
2025-05
itnonline.com
GE HealthCare to expand AIR Recon DL with ZTE and Silenz support
GE HealthCare is expanding its AIR Recon DL deep-learning reconstruction technology to support Zero Echo Time (ZTE) imaging, which is the basis for the MR bone imaging technique oZTEo, allowing for CT-like images without ionizing radiation.
2026-05

Videos

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Frequently Asked Questions

Bone VCAR (BVCAR) is a GE HealthCare software analysis package that uses a deep learning algorithm to assist in the analysis and visualization of CT spine data. It automates spine labeling and generates oblique and straightened reformats, aiming to simplify the reading experience and improve reporting efficiency for radiologists.
Bone VCAR offers automated spine labeling, automated generation of 3D traces for oblique and straightened reformat views, and automated generation of oblique views perpendicular to vertebral bodies and disc spaces. It is designed to be accessible for various exam types, including trauma, oncology, and general imaging, and can propagate labels across multiple loaded series.
GE HealthCare's Bone VCAR is a non-invasive image analysis software package that has received 510(k) premarket notification clearance from the FDA, indicating it is substantially equivalent to predicate devices. It is designed and manufactured under the Quality System Regulations of 21 CFR 820 and ISO 13485.
While Bone VCAR is designed to be highly accurate with over 90% labeling accuracy based on its deep learning algorithm, it is a post-processing application and does not have a user interface or include measurement tools. It provides DICOM reformat images for export.
Bone VCAR differentiates itself by utilizing a deep learning algorithm for automated spine labeling, which can identify and label segments of the spine or the entire spine in seconds, improving efficiency compared to manual labeling. While specific direct comparisons to other products are not detailed, GE HealthCare is a major player in medical imaging, competing with companies like Siemens Healthineers, Hologic, and Philips.
GE HealthCare typically offers software solutions like Bone VCAR through a subscription model. Pricing is based on current GE HealthCare rates, and subscription renewals are subject to price increases with prior written notice. The quantity on a quote indicates the number of CT scanners covered by the subscription.

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Suggest an Edit → | Report an issue | Last Verified: 2026-04-18 | First Added: 2026-04-18
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