FDA Radiology AI Devices

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.

Contour ProtégéAI is a software tool that assists trained medical professionals by automatically creating contours on CT and MR medical images using machine-learning algorithms. It helps in quantitative analysis, adaptive therapy, image segmentation of various anatomical regions including prostate via MR images, and supports radiation therapy treatment planning. The contours can be reviewed and edited with appropriate visualization software.

SwiftMR is a software medical device designed to automatically enhance brain MRI images by reducing noise and improving image sharpness. It processes MRI scans in DICOM format using advanced deep learning models, helping radiology professionals obtain clearer images for better diagnosis and clinical decision-making without interrupting their workflow.

StrokeSENS LVO is software that uses AI to analyze CT angiograms of the head to identify suspected large vessel occlusions, which can cause strokes. It assists healthcare providers by flagging potential LVO cases and sending notifications to specialists in near real-time, helping prioritize urgent cases and improve workflow efficiency. The software does not alter the original images and is intended to be used alongside existing diagnostic workflows.

VBrain-OAR is a software device from Vysioneer Inc. designed to assist trained radiotherapy personnel in brain tumor radiation therapy treatment planning by automatically generating contours of organs at risk in brain MRI images using AI deep learning. It also provides image registration functions to align multi-modality medical images. This device supports clinicians by providing initial contour outlines to improve the efficiency and consistency of radiation treatment planning, while still requiring final confirmation by qualified personnel.

SubtlePET is an advanced software tool designed to improve the quality of PET medical images by reducing noise. Utilizing deep learning algorithms, it enhances image clarity, aiding radiologists and nuclear medicine physicians in interpreting PET scans more accurately. This software integrates smoothly into existing radiology workflows by receiving, processing, and forwarding DICOM images, supporting multiple radiotracers used in PET imaging.

The Philips EPIQ and Affiniti Diagnostic Ultrasound Systems are advanced ultrasound machines that provide diagnostic imaging and fluid flow analysis for various body regions. They help clinicians visualize internal organs and blood flow in real time, assisting in the diagnosis and treatment of conditions related to the abdomen, heart, brain, musculoskeletal system, and more. The system also includes liver fat quantification tools to measure attenuation and hepato-renal index, supporting assessment of liver health.

Deep Learning Image Reconstruction by GE Healthcare is a software that uses deep neural networks to reconstruct high-quality CT images from X-ray transmission data. It helps radiologists by producing images with improved noise reduction, spatial resolution, and artifact suppression, suitable for head, whole body, cardiac, and vascular CT scans at routine clinical throughput.

Intelligent NR is a software function integrated with Canon's CXDI Control Software that uses machine learning-based noise reduction to enhance the quality of digital X-ray images. It helps clinicians obtain clearer images from conventional radiographic exams, improving diagnostic confidence especially in trauma, intensive care, and pediatric settings.