Hepatic VCAR
by GE Medical Systems SCS
· Based in United States →
— Automated segmentation and assessment of liver and liver lesions for efficient workflow.
Contact vendor for pricing
Regulatory Status Disclosed
Overview
Hepatic VCAR by GE Medical Systems SCS (part of GE HealthCare) is an advanced AI-powered software solution designed to assist clinicians in analyzing liver CT scans. It provides a complete reading workflow solution for detecting and monitoring liver lesions with exceptional flexibility and performance. The software utilizes deep learning algorithms for automated and editable 3D segmentation of the liver, liver lesions, and hepatic artery.
This tool helps visualize and measure the liver, its segments, and lesions, determine tumor burden, and efficiently manage lesions for longitudinal exams. It integrates with Spectral CT, allowing for quantification of Iodine to aid in lesion characterization when used with GSI datasets. Hepatic VCAR is intended for use by clinicians to process, review, archive, print, and distribute liver CT studies, facilitating faster and more precise liver evaluations and supporting surgical planning.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Automated liver segmentation (deep learning based)
- Intelligent user-guided lesion segmentation
- 3D visualization of liver, segments, and lesions
- Tumor burden calculation (linked to segment, lobe, or whole liver)
- Efficient management of lesions for longitudinal exams
- Integration with Spectral CT for iodine quantification
- Automated detection of portal venous phase
- Built-in lesion overlap detection and avoidance
- Intuitive editing tools for refinements
- Generation of clear, concise clinical reports
Use Cases
- Detection and diagnosis of liver lesions
- Monitoring treatment response for liver disease and cancer
- Pre-surgical planning for liver resections
- Volumetric analysis of liver, lobes, and segments
- Assessing liver morphology and changes over time
- Supporting liver embolization procedures
Details
| Category | Gastroenterology AI, Oncology AI, Radiology & Imaging AI |
| Pricing | Contact vendor for pricing |
| Deployment | On-premise (Advantage Workstation, AW Server, CT Scanners, PACS stations); potential for cloud deployment in the future. |
| Compliance | |
| BAA Available | Yes AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated Hepatic VCAR received FDA 510(k) clearance (K193281) on March 20, 2020. It is a Class II medical device intended for analyzing liver CT scans by providing automated and editable 3D segmentation of the liver, liver lesions, and hepatic artery, using deep learning algorithms. |
| Integrations | |
| EHR | Not specified |
| Specialties | General Surgery, Oncology, Radiology |
What the Web Says
Hepatic VCAR by GE Healthcare is a CT image analysis software designed to assist clinicians in assessing liver morphology, including lesions, and their changes over time. It utilizes deep learning algorithms for automated and editable 3D segmentation of the liver, liver lobes, segments, and hepatic arteries. The software aims to improve efficiency, consistency, and workflow in liver evaluations, particularly for surgical planning and lesion assessment.
Overall: PositiveStrengths
- Automated liver and hepatic artery segmentation using deep learning algorithms, enhancing efficiency and reproducibility.
- Provides initial 3D segmentation, vessel analysis, visualization, and quantitative analysis of liver anatomy.
- Assists in surgical planning and lesion evaluation through a guided workflow and tools for measuring liver, segments, and lesions.
- Offers intelligent user-guided segmentation algorithms for sizing liver lesions and allows for user adjustment and confirmation of segmentation.
- Integrates with Spectral CT for quantification of iodine to aid in lesion characterization.
- Supports loading multi-phase data over multi-time points for lesion comparison and management, with customizable and interactive reporting capabilities.
Limitations
- Robust clinical evidence for improving oncological control based on anatomical rationale remains limited.
- While AI has potential, most tools are weakly linked to pediatric clinical endpoints, and it has not yet been specifically designed, validated, or deployed in children.
- The approach, especially with advanced techniques like parenchymal-sparing anatomical hepatectomy, can be more complex than conventional methods, requiring higher standards of medical infrastructure and surgical expertise.
- No specific predicate devices were mentioned in one FDA document, which might indicate a newer approach or a lack of direct comparison with older, similar technologies.
- Limited information available from independent physician reviews, healthcare IT, tech reviewers, G2, Capterra, or Reddit outside of product descriptions and regulatory documents.
Based on reviews from: GE Healthcare, accessdata.fda.gov, X-ray Interpreter, ResearchGate, YouTube, Scribd
Last updated: 2026-07-21
Ratings & Reviews
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Press & Coverage
GE Healthcare
GE Healthcare Brings A 'Trusted Assistant' To Advancing Precision Medicine in Image Guided Therapies With The Award-Winning Allia Platform
GE Healthcare announced on June 13, 2022, the Allia platform, which leverages AI with Liver ASSIST Virtual Parenchyma, a 3D visualization software solution that includes Hepatic VCAR, to help clinicians with liver embolization procedures.
2022-06
Medical Alley
GE Healthcare Unveils New AI and Digital Technologies and Solutions to Help Solve ...
In November 2021, GE Healthcare introduced new AI-powered solutions, including Liver ASSIST Virtual Parenchyma, which incorporates Hepatic VCAR, to enhance diagnostic confidence and workflow efficiency.
2021-11
Medical Product Outsourcing
GE Healthcare Tops List of AI-Enabled Device Authorizations | Medical Product Outsourcing
GE Healthcare led the FDA's list of AI-enabled medical device authorizations in December 2022, with 42 clearances, including Liver ASSIST Virtual Parenchyma, which utilizes Hepatic VCAR.
2022-12
FDA
K193281 - FDA 510(k) Premarket Notification - Hepatic VCAR
On March 20, 2020, the FDA issued a 510(k) clearance for Hepatic VCAR, a CT image analysis software package designed for assessing liver morphology, including lesions, and their changes over time using automated segmentation and measurement tools.
2020-03
FDA
K133649 - 510(k) Premarket Notification - FDA
This FDA 510(k) premarket notification from June 22, 2026, lists Hepatic VCAR from GE Medical Systems SCS, indicating its regulatory clearance for market.
2026-06
PMC (PubMed Central)
Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence - PMC
A December 2023 peer-reviewed article discusses the use of Hepatic VCAR (GE Healthcare) for manual volumetric study in pre-operative living-donor liver transplantation evaluation, noting its reliance on operator expertise.
2023-12
ResearchGate
Relationship of the Presence of the Inferior Right Hepatic Vein with the Right Hepatic Vein Diameter and CT Liver Volumetry - ResearchGate
An April 2023 research paper utilized a deep learning-based automatic liver segmentation program, Hepatic VCAR, for liver volume analysis in patients undergoing triphasic CT for living liver donation.
2023-04
PMC (PubMed Central)
From research to reality: The role of artificial intelligence applications in HCC care - PMC
An April 2024 article highlights Hepatic VCAR as an AI tool from GE Medical Systems for segmentation, vessel analysis, visualization, and quantitative evaluation of liver anatomy in hepatocellular carcinoma (HCC) care.
2024-04
Videos
Product demos, reviews, and walkthroughs for Hepatic VCAR.
Radiofrequency Ablation - Pros & Cons
Frequently Asked Questions
Hepatic VCAR is designed as a complete reading workflow solution for detecting liver lesions with enhanced flexibility and performance. It automates tasks such as liver segmentation, detection of the portal venous phase, intelligent user-guided segmentation of liver lesions, and tumor burden calculation, facilitating efficient and consistent reporting.
While specific regulatory approvals (e.g., FDA, CE Mark) for Hepatic VCAR are not detailed in the provided snippets, commercial medical AI solutions generally require rigorous validation and regulatory clearance for clinical use. Compliance with data privacy regulations, such as HIPAA or GDPR, is crucial for any AI system handling patient data, and vendors must ensure secure data handling and processing.
Alternatives to Hepatic VCAR include other AI-driven tools for liver assessment, such as Quibim's QP-Liver, which quantifies tissue fat and iron levels through MRI scans. Generally, AI in hepatology offers various applications like fibrosis staging, lesion detection, and volumetry across different imaging modalities (ultrasound, CT, MRI), with different tools potentially specializing in specific aspects or disease conditions.
Healthcare AI imaging pricing models commonly include subscription-based, per-scan (pay-per-use), value-based, or enterprise licensing approaches. The total cost of ownership is influenced by factors such as technological complexity, clinical value, integration challenges, necessary hardware investments, and ongoing maintenance and support.
While Hepatic VCAR boasts a high success rate for automatic liver segmentation, general limitations of AI in liver disease diagnosis include data heterogeneity, the need for extensive multi-center validation, and challenges with model interpretability. AI models may also have limitations in detecting early-stage disease or in specific patient populations, and their utility and impact on patient outcomes still require assessment in large-scale studies.
Physician oversight remains crucial when using AI tools like Hepatic VCAR, as these systems are designed to assist rather than replace clinical judgment. The introduction of AI into clinical use requires careful validation and scrutiny of the algorithms, and physicians are ultimately responsible for the final diagnostic and treatment decisions.
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| Last Verified: 2026-04-20 | First Added: 2026-04-20