FDA Radiology AI Devices

AI-Rad Companion (Pulmonary) by Siemens Healthcare GmbH is software that uses machine learning and deep learning to analyze previously acquired chest CT scans. It helps radiologists and physicians segment lungs and lung lobes, identify and measure solid and sub-solid lung nodules, and track changes over time. This supports clinicians in assessing lung diseases, improving workflow efficiency and diagnostic accuracy.

LungQ v3.0.0 is a software tool that helps physicians analyze CT scans of the lungs by automatically segmenting lung structures, measuring lung volumes and densities, evaluating fissures, and generating reports. It supports diagnosis and monitoring of lung conditions by providing quantitative assessments from CT scans, streamlining clinical workflows.

BriefCase-Quantification by Aidoc Medical is an AI-driven software designed to analyze non-cardiac-gated, non-contrast CT scans including the heart to detect and categorize coronary artery calcification (CAC). It provides physicians with a four-category risk assessment for coronary artery disease along with preview images of detected calcium, assisting clinical evaluation but not replacing full image review or clinical judgement.

The iCAC Device is a software tool that analyzes routine chest CT scans to automatically detect and quantify coronary artery calcium, which helps physicians assess cardiovascular risk. It provides outputs such as calcium segmentation visualizations and quantitative calcium scores during standard clinical workflows, assisting physicians without replacing original reports or scans.

AI-Rad Companion (Pulmonary) is AI-powered software that analyzes chest CT scans to help radiologists and clinicians identify and measure lung structures and lesions. It segments lungs and lung lobes, detects lung nodules, compares current images to prior scans for lesion follow-up, and quantifies pulmonary density. These features support the clinical assessment and monitoring of lung diseases, improving workflow efficiency and diagnostic accuracy.

Al-Rad Companion (Pulmonary) is an AI-powered software by Siemens that processes CT images of the lungs to assist clinicians in quantitatively and qualitatively analyzing lung diseases. It segments lungs and lobes, detects lung lesions and areas of abnormal tissue density, and provides measurements to support radiologists and physicians in emergency, specialty, and general care settings. This helps streamline and improve lung disease evaluation.

VIDA|vision is an interactive imaging software that helps physicians analyze CT scans of the lungs. It uses deep learning algorithms to automatically segment lung regions and provides detailed quantitative analyses to assist in diagnosing and monitoring lung diseases such as lung cancer, COPD, and asthma. This software reconstructs two-dimensional CT images into 3D views and generates detailed reports, streamlining the workflow and reducing manual effort for clinicians.

Broncholab is a software tool that helps doctors analyze CT scans of the lungs by providing detailed quantitative information about lung volume, airway volume, lung density, and other pulmonary tissue characteristics. It assists in the diagnosis and follow-up of lung abnormalities by creating 3D models and measurements from CT images, supporting clinical decision-making.

AI-Rad Companion (Cardiovascular) is an AI-powered software that analyzes previously acquired CT chest images to segment the heart and aorta, quantify coronary calcium, and measure aortic diameters. It assists clinicians by providing quantitative and qualitative cardiovascular assessments to support diagnosis and evaluation in emergency, specialty, and general practice care.

AI-Rad Companion (Pulmonary) is advanced software designed to analyze CT images of the lungs. It helps clinicians by segmenting lung regions, measuring lung volumes, identifying areas with abnormal tissue density, and detecting lung lesions including solid pulmonary nodules. This enables efficient and precise assessment of lung disease from existing CT scans, supporting better diagnosis and treatment decisions in emergency and specialty care.