Hepatic VCAR

GE HealthCare
Hepatic VCAR Model Card

Hepatic VCAR provides automated segmentation and assessment of liver, liver lesions, and vasculature which helps enable an efficient and consistent workflow.

Product Details
Model Identifier
Manufacturer
GE HealthCare
Product
Hepatic VCAR
Version
Unknown
Date Cleared
03/20/2020
FDA Submission No.
Category
MIMPS
Model Characteristics
Inclusion Criteria
CT Liver
Exclusion Criteria
Unspecified
Instructions for Use
Not available
Indications for Use
Indication of Use
Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data. It is designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver, liver lobe, liver segments and liver lesion segmentation and measurement.
Intended User
Clinicians
Age
Unknown
Anatomy
Abdomen
Modality
CT
Output
Lung and lobe segmentation to obtain threshold-based volume measurements; bronchial tree segmentation and tracking to determine wall thickness measurements; lung maps based on HU values to help the physician in determining the location and extent of disease across both lungs a well as each lobe. Additionally, GSI datasets can be used for the evaluation of relative perfusion within the lungs.
Details on Training Data Sets
Details on Training Data Sets
No. of Cases
Unknown
Age Range (Years)
Unknown
Sex (%)
  • Female: Unknown
  • Male: Unknown
  • Unknown: Unknown
Output
Unknown
Race (%)
  • White: Unknown
  • Black or African American: Unknown
  • American Indian or Alaska Native: Unknown
  • Asian: Unknown
  • Native Hawaiian or Other Pacific Islander: Unknown
  • Unknown: Unknown
Ethnicity (%)
  • Hispanic or Latino: Unknown
  • Not Hispanic or Latino: Unknown
  • Unknown: Unknown
Geographic Region (%)
  • USA: Unknown
  • International: Unknown
  • Unknown: Unknown
Scanner Manufacturer(s)
Unknown
Scanner Model(s)
Unknown
Model Performance
Study Type
Performance Testing Type
Stand-Alone Performance
Standalone Model Performance
Reference Standard (Ground Truth)
Interpretation by Reviewing Clinician
No. of Cases
Unknown
Age Range (Years)
Unknown
Sex (%)
  • Female: Unknown
  • Male: Unknown
  • Unknown: Unknown
Race (%)
  • White: Unknown
  • Black or African American: Unknown
  • American Indian or Alaska Native: Unknown
  • Asian: Unknown
  • Native Hawaiian or Other Pacific Islander: Unknown
  • Unknown: Unknown
Ethnicity (%)
  • Hispanic or Latino: Unknown
  • Not Hispanic or Latino: Unknown
  • Unknown: Unknown
Geographic Region (%)
  • USA: Unknown
  • International: Unknown
  • Unknown: Unknown
Output
Unknown
Scanner Manufacturer(s)
Unknown
Scanner Model(s)
Unknown
No. of Sites
Unknown
Model Accuracy
Unknown
Model Sensitivity
Not provided
Model Specificity
Not provided
Reader Study Performance
No. of Readers
N/A
No. of Cases
N/A
No. of Sites
N/A
Output
N/A
Age Range (Years)
N/A
Sex (%)
  • Female: N/A
  • Male: N/A
  • Unknown: N/A
Race (%)
  • White: N/A
  • Black or African American: N/A
  • American Indian or Alaska Native: N/A
  • Asian: N/A
  • Native Hawaiian or Other Pacific Islander: N/A
  • Unknown: N/A
Ethnicity (%)
  • Hispanic or Latino: N/A
  • Not Hispanic or Latino: N/A
  • Unknown: N/A
Geographic Region (%)
  • USA: N/A
  • International: N/A
  • Unknown: N/A
Scanner Manufacturer(s)
N/A
Scanner Model(s)
N/A
Model Accuracy
N/A
Model Sensitivity
N/A
Model Specificity
N/A
Model Limitations, Warnings, & Precautions
Model Limitations, Warnings, & Precautions
Supported Scanner Manufacturer(s)
Unknown
Slice Thickness
Unknown
Contrast Use
Unknown
MRI Field Strength
Unknown
Reconstruction Kernel Used
Unknown
Alternative Choices
Alternative Choices
Previous Version(s)
Unknown
Contact Information
Contact Information
Point of Contact Name
Unknown
Email
Unknown
Additional Details
Related Use Cases
Unknown
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