TrueFidelity Deep Learning Image Reconstruction

GE HealthCare
TrueFidelity Deep Learning Image Reconstruction Model Card

Deep learning image reconstruction promises unparalleled benefits for patients, along with the radiologists and technologists dedicated to their care. GE HealthCare trained its reconstruction engine using a library of thousands of low noise, filtered back projection (FBP) images considered the gold standard of image quality. TrueFidelity Deep Learning Image Reconstruction images are more than a radical, next-generation improvement. They elevate the vision of what you and TrueFidelity can achieve together.

Product Details
Model Identifier
Manufacturer
GE HealthCare
Product
TrueFidelity Deep Learning Image Reconstruction
Version
Unknown
Date Cleared
04/20/2023
FDA Submission No.
Category
MIMPS
Model Characteristics
Inclusion Criteria
CT Images
Exclusion Criteria
Unspecified
Instructions for Use
Not available
Indications for Use
Indication of Use
The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.
Intended User
Unknown
Age
Adult and Pediatric
Anatomy
Whole Body
Modality
CT
Output
Image reconstruction
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
Unknown
Standalone Model Performance
Reference Standard (Ground Truth)
N/A
No. of Cases
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
Output
N/A
Scanner Manufacturer(s)
N/A
Scanner Model(s)
N/A
No. of Sites
N/A
Model Accuracy
N/A
Model Sensitivity
N/A
Model Specificity
N/A
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
All Products And Platforms from GE HealthCare