FDA Radiology AI Devices

The MAGNETOM Avanto Fit, MAGNETOM Skyra Fit, MAGNETOM Sola Fit, and MAGNETOM Viato.Mobile are MRI systems designed to produce detailed diagnostic images and spectra of the head, body, and extremities. They include advanced AI-based features such as Deep Resolve Boost and Deep Resolve Sharp to enhance image quality and speed, aiding radiologists in diagnosis and treatment planning.

AI-Rad Companion Organs RT is an AI-powered software that automatically segments predefined organs-at-risk from CT and MR images. It helps radiation oncologists by generating contours used in treatment planning, streamlining workflow and improving efficiency. The contours produced are reviewed and edited by trained professionals before use in radiation therapy.

Prostate MR AI (VA10A) is a plug-in AI software device designed to assist radiologists in interpreting prostate MRI scans. It automatically segments the prostate gland, detects suspicious lesions, and assigns scores to help radiologists identify and classify potential prostate cancer. This tool supports clinicians by augmenting their workflow and improving diagnostic accuracy.

Syngo Carbon Enterprise Access (VA40A) is a software-only medical device developed by Siemens Healthcare GmbH for use in healthcare institutions. It provides an enterprise-wide web application to display and render DICOM and non-DICOM medical data, supporting image and result distribution for diagnostic use by trained medical professionals. The software facilitates standardized data access without controlling image acquisition and enhances clinical workflows with a user-friendly interface. It does not include automated diagnostic functions but supports quality display and management of medical images and documents.

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.

Syngo Carbon Clinicals is a software package that provides advanced visualization tools to help healthcare professionals prepare, measure, and analyze medical images from modalities such as CT and MRI. It supports manipulation and communication of clinical data, enabling qualitative and quantitative assessment to assist in diagnosis and treatment planning.

syngo.CT Lung CAD (Version VD30) is a computer-aided detection tool developed by Siemens Healthcare GmbH to help radiologists identify solid and subsolid pulmonary nodules in chest CT scans. The software highlights regions that might be overlooked, improving the accuracy and efficiency of lung nodule detection during radiological review. It can operate as a first or second reader and includes a mode to filter out subsolid and fully calcified nodules for focused analysis.

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.

AI-Rad Companion Brain MR is software that assists clinicians by automatically analyzing brain MRI scans. It segments various brain structures, quantifies their volumes, and highlights white matter hyperintensities, comparing results to normal data. This helps radiologists make more accurate and efficient evaluations of brain health.

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.