FDA Radiology AI Devices

Eclipse II with Smart Noise Cancellation is software that improves digital X-ray images by reducing noise using advanced AI technology. It enhances images acquired with digital radiography systems, helping radiologists see clearer images for better diagnosis of adult and pediatric patients, excluding mammography.

DV.Target is a software product that uses machine learning algorithms to automatically delineate organs-at-risk on CT images. It supports radiation therapy planning by generating organ contours that clinicians can review and edit in a treatment planning system. The software operates on a server and integrates with clinical workflows through DICOM image routing.

The SIS System (Version 5.1.0) is a medical imaging software that helps surgeons, neurologists, and radiologists visualize specific brain structures such as the subthalamic nuclei and globus pallidus from MRI and CT scans. It uses advanced deep learning models to create 3D anatomical models to aid in stereotactic surgical planning and lead placement, improving visualization and accuracy during procedures.

The syngo.CT Lung CAD by Siemens Healthcare GmbH is a software tool designed to assist radiologists in detecting solid and subsolid lung nodules on chest CT scans. It uses advanced AI algorithms based on convolutional neural networks to highlight suspicious areas, reducing the chance of missed nodules. The software integrates with existing medical imaging platforms, improving clinicians' efficiency and accuracy in lung nodule detection, a critical task in lung cancer screening and diagnosis.

The MAGNETOM Vida, MAGNETOM Sola, MAGNETOM Lumina, and MAGNETOM Altea are magnetic resonance imaging systems designed to produce detailed cross-sectional images and spectra of the head, body, and extremities. These systems aid clinicians in diagnosis by providing high-quality images and offer specialized features such as interventional imaging support and automated workflows, improving reproducibility and efficiency.

The Aquilion ONE (TSX-306A/3) V10.4 with Spectral Imaging System is a whole-body CT scanner that can capture detailed cross-sectional volume images of organs in a single rotation. It uses advanced AI algorithms such as deep convolutional neural networks to improve image quality and reduce noise, helping clinicians to better visualize anatomy and potentially optimize iodinated contrast media use. The spectral imaging system acquires images at different energy levels nearly simultaneously to differentiate tissues and materials, assisting in diagnosis and treatment planning.

Viz ICH is an AI-powered software tool that analyzes non-contrast CT brain images to detect suspected intracranial hemorrhages. It sends notifications to neurovascular or neurosurgical specialists to facilitate earlier patient review and intervention. The software is integrated with a mobile application for previewing images and managing notifications, though image previewing is non-diagnostic and full diagnosis relies on standard clinical evaluation.

VBrain is an AI-powered software device that helps trained medical professionals by automatically generating initial contours of known brain tumors on MRI scans. It supports radiation therapy planning by providing preliminary tumor outlines for professionals to review and adjust, streamlining the clinical workflow for brain tumor treatment planning.

ProFound AI Software V3.0 is an AI-based medical imaging software that aids radiologists by detecting and highlighting suspicious soft tissue densities and calcifications in 3D digital breast tomosynthesis images. It provides confidence scores that help clinicians identify potentially malignant findings more quickly and accurately during breast cancer screening and diagnosis.

Oncospace is a software system designed to assist radiation oncologists and medical dosimetrists in planning radiotherapy treatments for prostate, thoracic, pancreas, and head & neck cancers. It uses machine learning to predict personalized dose goals for organs at risk, helping clinicians optimize treatment plans more efficiently and safely, potentially reducing the number of iterations needed for plan approval.