FDA Radiology AI Devices

Deep Learning Image Reconstruction (DLIR) by GE Medical Systems is an AI-powered CT image reconstruction method that uses a deep neural network to produce high-quality cross-sectional images of the head and whole body. It supports multiple CT acquisition types and enhances image quality by reducing noise and artifacts, helping clinicians get clearer and more accurate imaging results.

The Revolution Ascend is a CT scanner designed to generate detailed cross-sectional images of various body parts including the head, whole body, cardiac, and vascular areas. It uses x-ray transmission data taken at different angles and planes. The device supports multiple acquisition modes and post-processing of images, aiding clinicians in diagnosing disease, trauma, and abnormalities, as well as planning and monitoring therapy.

syngo.CT Brain Hemorrhage is an AI-driven software that assists radiologists by analyzing non-contrast head CT scans to prioritize cases suspicious for acute intracranial hemorrhage. It flags findings to improve workflow efficiency by enabling faster review of urgent cases but is not intended for standalone diagnosis.

syngo.CT Extended Functionality is a software suite from Siemens that provides advanced visualization tools for CT and possibly MRI medical images. It helps technicians and physicians perform qualitative and quantitative measurements, facilitates advanced clinical evaluations using various extensions, and supports processing to aid in diagnosis.

Precise Image is an AI-powered reconstruction software for computed tomography (CT) systems developed by Philips. It produces high-quality images with lower radiation dose and noise, improving the detectability of low contrast features. This helps clinicians obtain clearer diagnostic images from CT scans of the head, body, and vascular regions, enhancing patient safety by reducing radiation exposure while maintaining image quality.

AIBOLIT 3D+ is a software system that takes CT scan images and processes them into detailed 3D visual models of anatomical structures. This tool helps doctors plan surgeries, train, and educate patients by enabling interactive viewing of organs and tissues from multiple angles and with enhanced clarity. It supports better decision-making and may reduce errors during procedures.

RT-Mind-AI is a software tool designed to automatically segment organs-at-risk in non-contrast CT images for patients undergoing radiation therapy. It helps radiation oncologists by providing accurate anatomical contours that support treatment planning and evaluation, improving workflow and precision.

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.

Advanced Algorithms for Treatment Management Applications (AATMA) is a software library that uses machine-learning convolutional neural networks to automatically segment medical images. It provides derived data sets for use in radiation therapy treatment planning, accessible via an API and intended to help clinicians efficiently generate and review treatment contours from imaging data.

FlightPlan for Liver is a post-processing software that helps physicians analyze 3D X-ray angiography images to visualize the liver's vasculature and identify arteries near hypervascular lesions. It incorporates AI algorithms, including deep learning-based liver segmentation, to support the planning of liver embolization procedures, improving physician workflow and precision in treatment planning.