Bone VCAR (BVCAR)

by GE Medical Systems SCS  · Based in United States → — Automated spine labeling with oblique and straightened reformat generation to improve ease of reading and reporting.
Neurosurgery Orthopedics Radiology
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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

DICOM Support & Standards
Bone VCAR is included in GE Healthcare's Volume Viewer DICOM Conformance Statement, indicating support for DICOM standards.
PACS Integration Method
As an application within GE Healthcare's advanced visualization (AW) suite, Bone VCAR is designed to integrate with GE's PACS platforms, such as True PACS and Centricity PACS, to streamline workflows and manage imaging data.
Reading Room Workflow Impact
Bone VCAR is designed to simplify the reading experience and improve reporting efficiency through automated spine labeling and the generation of oblique and straightened reformat views. It can identify and label segments or the entire spine in seconds, is accessible for various exam types (trauma, oncology, dedicated spine, general imaging), and allows for easy editing of the centerline.
AI Model Architecture (deep learning approach)
The tool utilizes a deep-learning algorithm for automated spine labeling, trained on global datasets with a broad range of acquisition parameters. It is based on GE Healthcare's Edison platform, which is designed to accelerate AI development and adoption.
Processing Speed (per study)
Bone VCAR can label the spine for volumes of 300mm or less in under 5 seconds.
FDA Clearance Pathway (510k/De Novo)
Bone VCAR received 510(k) clearance from the U.S. FDA in April 2019.
Supported Modalities (CT/MRI/X-ray/US)
Bone VCAR is primarily designed for use with CT images, supporting both full spine acquisitions and limited acquisitions containing segments of the spine. It is compatible with single energy and Gemstone Spectral Imaging (GSI) acquisition methods.
Sensitivity & Specificity Data
The deep learning algorithm used in Bone VCAR achieves >90% labeling accuracy.
RSNA/ACR Validation
Bone VCAR was introduced at RSNA 2018, highlighting GE Healthcare's focus on AI in radiology.
Physician Tip

Leverage Bone VCAR's automated spine labeling and reformat generation to significantly expedite spine assessment and improve reporting efficiency. Utilize its broad applicability across various CT exam types (trauma, oncology, dedicated spine, general imaging) to standardize and streamline your workflow. The ability to easily edit the centerline ensures accurate placement even in complex cases, and label propagation across multiple series saves valuable review time.

Bone VCAR is an integral part of GE Healthcare's advanced visualization (AW) applications, built on the Edison platform. This facilitates seamless integration within the GE imaging ecosystem, including GE Healthcare's CT systems and AW workstations. Expect robust integration capabilities with GE's PACS solutions (e.g., True PACS, Centricity PACS) for efficient image management and streamlined data flow within your radiology department.

Details

Category Radiology & Imaging AI
Pricing Unknown
DeploymentPost-processing application for Advantage Workstation (AW) platform, CT Scanner, Cloud, or PACS stations.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Bone VCAR (BVCAR) received FDA 510(k) clearance (K183204) on April 8, 2019. It is classified as a Class II medical device (21 CFR 892.1750) and is intended as a non-invasive image analysis software package to aid in the assessment and reporting efficiency of CT images that include the spine.
Integrations
EHR Not specified
Specialties Neurosurgery, Orthopedics, Radiology

What the Web Says

GE Healthcare's Bone VCAR (BVCAR) is a deep-learning-based software analysis package designed to assist clinicians in the analysis and visualization of CT spine data. It automates spine labeling and generates oblique and straightened reformat views, aiming to improve reading and reporting efficiency for various exam types, including trauma, oncology, and general imaging.

Overall: Mixed

Strengths

  • Automated spine labeling with high accuracy (>90%) based on deep learning algorithms.
  • Automated generation of oblique and straightened reformat views, improving ease of reading and reporting.
  • Significant improvement in workflow efficiency, reducing reconstruction time per exam by 5-10 minutes.
  • Consistent and standardized results, reducing user-dependent variability in reconstructions.
  • Integrated into existing scan protocols on the operator console, eliminating the need for additional workstations.
  • Allows radiologists to start reading images immediately as labeling is automated and available in PACS.

Limitations

  • General negative sentiment on Reddit regarding GE Healthcare's customer service, high costs for support, and perceived poor management culture.
  • Some users on Reddit express frustration with GE Healthcare's business practices, including high markups on parts and pressure to purchase service contracts.
  • Concerns about unachievable goals and objectives for employees within GE Healthcare.
  • Lack of transparency in pay increases for GE Healthcare employees.
  • The product or its specific features may not be available in all markets.
  • No specific negative reviews directly related to BVCAR's performance were found in the search results, but general dissatisfaction with GE Healthcare as a company could indirectly impact user perception.

Based on reviews from: GE Healthcare (gehealthcare.com), accessdata.fda.gov, YouTube, Reddit, Indeed, PubMed

Last updated: 2026-07-18

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Press & Coverage

AuntMinnie
GE gets FDA nod for deep-learning CT reconstruction
GE Healthcare received FDA clearance for its Deep Learning Image Reconstruction (DLIR) engine for CT, along with Bone VCAR, SnapShot Freeze 2, and Thoracic VCAR with GSI Pulmonary Perfusion applications. Bone VCAR uses a deep-learning algorithm to identify and label vertebrae for faster spine assessment and improved reporting efficiency.
2019-04
Medimaging.net
First Deep Learning-Based Image Reconstruction Technology Receives FDA Clearance
GE Healthcare's Deep Learning Image Reconstruction (DLIR) engine received 510(k) clearance from the U.S. FDA, along with Bone VCAR, Thoracic VCAR with GSI Pulmonary Perfusion, and SnapShot Freeze 2. Bone VCAR utilizes a deep-learning algorithm for automatic identification and labeling of vertebrae to enhance spine assessment and reporting efficiency.
2019-05
DAIC (Diagnostic and Interventional Cardiology)
FDA Clears GE's Deep Learning Image Reconstruction Engine
The FDA granted 510(k) clearance to GE Healthcare's Deep Learning Image Reconstruction engine and three other CT applications, including Bone VCAR. Bone VCAR, an Edison application, uses a deep-learning algorithm to automatically identify and label vertebrae for faster spine assessment and improved reporting efficiency.
2019-04
DOTmed Healthcare Business News
FDA clears GE's AI-based CT image reconstruction technology
GE Healthcare received FDA clearance for its Deep Learning Image Reconstruction engine and also for Bone VCAR, SnapShot Freeze 2, and Thoracic VCAR with GSI Pulmonary Perfusion. Bone VCAR, an Edison platform application, employs a deep-learning algorithm to automatically identify and label vertebrae in spine images.
2019-04
accessdata.fda.gov
GE Medical Systems SCS FDA 510(k) Premarket Notification Submission for Bone VCAR
This regulatory announcement details the FDA's 510(k) clearance for Bone VCAR, a post-processing application for CT images. It uses a deep learning technique to automatically label the spine, supporting clinicians in visualization and improving reporting efficiency for various exams.
2019-04
GE Healthcare
Bone Vcar Advanced Visualization for Computed Tomography | GE HealthCare (United States)
Bone VCAR is a software analysis package utilizing a deep learning algorithm to assist in the analysis and visualization of CT spine data, offering automated spine labeling and generation of oblique and straightened reformat views to improve reading and reporting efficiency.
2024-03
GE Healthcare
GE Healthcare Announces the First U.S. FDA 510(k) Cleared Deep Learning Image Reconstruction for TrueFidelityu2122 CT Images
GE Healthcare announced FDA 510(k) clearance for its Deep Learning Image Reconstruction for TrueFidelityu2122 CT Images, along with clearances for Bone VCAR, SnapShot Freeze 2, and Thoracic VCAR with GSI Pulmonary Perfusion CT applications.
2019-04
PMC (PubMed Central)
Computational healthcare: Present and future perspectives (Review)
This review article discusses the present and future perspectives of computational healthcare, listing Bone VCAR by GE Medical Systems as a 2019 radiology diagnosis application utilizing AI.
2023-03

Videos

Product demos, reviews, and walkthroughs for Bone VCAR (BVCAR).

View all on YouTube

Frequently Asked Questions

Bone VCAR is a deep-learning based AI application designed for automated spine labeling and the generation of oblique and straightened reformatted views from CT data. It integrates into the imaging workflow to enhance reading efficiency and reporting for various exam types, including trauma, oncology, and dedicated spine studies.
Healthcare AI solutions must adhere to stringent data privacy regulations such as HIPAA and GDPR through robust data anonymization, secure handling, and strict access controls. Vendors are typically responsible for ensuring their AI tools are compliant and often undergo certifications like ONC to maintain patient safety and privacy.
Traditional alternatives involve manual spine labeling and reformatting performed by radiologists or technologists, which can be labor-intensive and time-consuming. Bone VCAR offers automated, rapid identification and labeling with over 90% accuracy, aiming to provide greater consistency and efficiency compared to manual methods.
Pricing models for AI in medical imaging commonly include subscription-based, pay-per-use, or hybrid structures. The potential Return on Investment (ROI) for Bone VCAR could arise from improved reading and reporting efficiency, reduced radiologist workload, and faster turnaround times, though economic value is highly context-dependent.
While Bone VCAR reports over 90% labeling accuracy, AI systems can have limitations such as potential performance issues with poor image quality, biases from training data, or in cases of rare pathologies. It functions as an assistive tool, and human oversight remains critical for final diagnostic decisions and interpretations.
Bone VCAR's deep learning algorithm was trained on extensive global datasets encompassing a wide range of acquisition parameters to ensure broad applicability and consistency. Its clinical efficacy is supported by its ability to automate complex tasks and streamline reporting, with regulatory clearances (e.g., FDA) serving as key indicators of rigorous validation.
Typically, vendors provide comprehensive technical support, regular software updates, and new feature releases for their AI solutions. These ongoing updates are essential for maintaining optimal performance, addressing emerging challenges, and ensuring the AI tool remains effective and compliant with evolving standards.

Compare Bone VCAR (BVCAR)

VS
Axial3D Insight vs Bone VCAR (BVCAR)
Radiology & Imaging AI Adjacent · 65/100
Tool A, Axial3D Insight, focuses on converting 2D medical images into 3D digital and physical models for comprehensive surgical planning and patient communication. In contrast, Tool B, Bone…
VS
CINA-CSpine vs Bone VCAR (BVCAR)
Radiology & Imaging AI Adjacent · 65/100
CINA-CSpine is an AI-powered triage tool specifically designed for the rapid detection and notification of acute cervical spine fractures on CT scans, focusing on diagnostic accuracy. In contrast,…
VS
QuickRad vs Bone VCAR (BVCAR)
Radiology & Imaging AI Adjacent · 65/100
QuickRad is a comprehensive, AI-integrated PACS and RIS system designed for end-to-end radiology workflow optimization across various modalities. In contrast, Bone VCAR is a specialized deep learning AI…
VS
D2P vs Bone VCAR (BVCAR)
Radiology & Imaging AI Adjacent · 60/100
D2P by 3D Systems is designed for converting 2D medical images into editable 3D anatomical models for advanced visualization, surgical planning, and 3D printing. In contrast, Bone VCAR…

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