FDA Radiology AI Devices

Spine Auto Views is a software tool that automatically processes CT images of the spine to create anatomically focused reformatted views and labels vertebrae and disc spaces, facilitating quick and consistent review for clinicians. It utilizes deep learning algorithms to generate multiple reformatted image planes without user interaction and exports the results automatically for radiologist reading.

Spectral Bone Marrow is an automated deep learning-based software designed for spectral CT images of the body and extremities. It segments bone regions and creates enhanced, color-coded images to help radiologists better visualize bone marrow. This assists in diagnosing traumatic and non-traumatic bone conditions more efficiently by providing improved image visualization and an automated clinical workflow.

AI-Rad Companion (Musculoskeletal) is an AI-powered software that analyzes previously acquired CT images to assist radiologists and physicians in evaluating musculoskeletal diseases. It segments, labels vertebrae, measures vertebral heights, and computes mean Hounsfield values to provide detailed quantitative and qualitative analysis, improving assessment and workflow efficiency for clinicians.

HealthOST is an AI-powered image processing software that analyzes CT scans of the spine to help clinicians detect and assess musculoskeletal diseases in patients aged 50 and older. It labels vertebrae, measures vertebral height loss, and calculates bone density using Hounsfield Units, providing quantitative data to aid diagnosis and treatment planning without replacing clinical judgment.

Al-Rad Companion (Musculoskeletal) is AI-powered software designed to analyze CT images of the spine. It supports clinicians by automatically segmenting and labeling vertebrae, measuring vertebral heights, and calculating the mean Hounsfield unit values within vertebrae, aiding in musculoskeletal disease evaluation and assessment.

Bone VCAR is a software tool designed to assist clinicians in reviewing CT images that include the spine. It uses deep learning to automatically label vertebrae and provides optimized display settings to improve visualization and reporting efficiency across multiple care areas such as trauma and oncology.