FDA Radiology AI Devices

syngo.CT Dual Energy is a post-processing software application for CT scans that uses dual-energy imaging techniques combining low and high energy X-ray spectra to enhance visualization of different materials in the body. It helps clinicians by providing advanced imaging features for better evaluation of anatomical structures and pathologies, including brain hemorrhage visualization, lung perfusion, kidney stone analysis, and more, improving diagnostic accuracy without requiring additional scans.

Aquilion ONE (TSX-308A/3) V1.5 is a whole-body multi-slice helical CT scanner capable of imaging entire organs in a single rotation. It uses AI-powered deep learning reconstruction and noise reduction algorithms to enhance image quality, reduce radiation dose, and minimize artifacts. This helps clinicians obtain clearer diagnostic images for various body regions including the head, chest, abdomen, pelvis, cardiac, extremities, and lungs, supporting better patient diagnosis and treatment planning.

IQ-UIP is an AI-powered computer-aided software designed to analyze non-contrast chest CT scans to detect and notify specialists about radiological findings suggestive of usual interstitial pneumonia (UIP), a type of fibrotic lung disease. It works alongside standard radiological workflows to help clinicians by providing summarized findings via a worklist application, assisting in the evaluation of interstitial lung disease without altering original images or providing diagnostic conclusions itself.

BriefCase-Triage by Aidoc Medical, Ltd. is an artificial intelligence software that analyzes CT scans including the ribs to identify suspected acute rib fractures. It alerts clinicians with notification and preview images to help prioritize cases for faster review, aiding workflow triage without altering original images or serving as a diagnostic tool. This helps radiologists address urgent findings more efficiently.

IRISeg is a software application developed by Intuitive Surgical that utilizes machine learning to assist with the segmentation of contrast-enhanced kidney CT scans and generates 3D models for preoperative surgical planning and intraoperative display. It provides both automatic and manual tools to segment kidney structures, helping clinicians visualize anatomy more efficiently and accurately for better patient management.

AutoContour (Model RADAC V4) by Radformation, Inc. is a software tool designed to automatically contour anatomical structures on CT and MR images to aid radiation treatment planning. Using machine learning, it helps clinicians create accurate contours of organs and tissues, streamlining the preparation process for radiation therapy and improving planning precision.

The EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) is an AI-powered software tool that analyzes non-contrast head CT images to identify signs of midline shift. It assists radiologists by flagging cases that may need urgent attention, helping prioritize patient care without altering original images or making direct diagnostic decisions.

DrAid™ for Liver Segmentation is a web-based AI software that assists healthcare professionals in analyzing liver images from CT scans. It generates semi-automated liver segmentation and allows physicians to manually refine these segmentations and identify lesions, aiding in evaluation and treatment planning without providing direct diagnosis.

ClearRead CT CAC is software that helps physicians assess coronary artery calcification on non-contrast, non-gated chest CT scans. It automatically detects and quantifies calcified coronary lesions, providing an Agatston score and categorization to aid in cardiovascular risk assessment. This allows clinicians to efficiently obtain quantitative calcium scoring to help guide patient management, supplementing but not replacing clinical expert review.

RadUnity (Version 1.0.0) is a software device designed to centralize, manage, standardize, and network CT images using the DICOM protocol. It reformats CT medical images according to user-defined preferences to ensure consistent image formatting, thus supporting qualified radiology professionals in preparing images for interpretation. It is not intended for primary diagnostic review nor for mammographic image interpretation.