FDA Radiology AI Devices

The Cranial Navigation and Cranial EM System by Brainlab AG is an image-guided surgical navigation system designed to assist surgeons during cranial and craniofacial procedures. It uses imaging data like CT, MR, X-Ray, and ultrasound to track surgical instruments relative to patient anatomy. The system integrates artificial intelligence via machine learning algorithms to detect brain abnormalities and anatomical landmarks in MR images to enhance registration accuracy and view centering, helping surgeons perform more precise navigation in complex brain surgeries.

OptimMRI is a software tool that helps medical professionals analyze pre-operative brain MRI images by generating 3D annotated models. It supports neurosurgical planning by localizing key brain regions, helping doctors visualize and plan treatments more precisely.

uOmnispace is a medical image post-processing software solution that supports viewing, manipulation, annotation, and communication of various medical images from multiple modalities including CT, MRI, PET and DR. It includes AI-based segmentation tools like rib segmentation, and enables remote multi-user access and integration with medical imaging systems. It helps clinicians efficiently analyze and report on diagnostic images.

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.

Brainlab Elements is a suite of software applications that process and analyze medical image data from various modalities to assist clinicians in planning surgeries and radiation treatments. It offers tools for image registration (fusion), segmentation (contouring), diffusion tensor imaging (fibertracking), blood oxygen level dependent MRI analysis (BOLD MRI mapping), and cerebrovascular image fusion (Image Fusion Angio). These capabilities help improve precision and understanding of patient anatomy and function during clinical procedures.

The ECHELON Synergy MRI system is an advanced 1.5 Tesla MRI scanner that generates high-quality diagnostic images of the head, body, spine, and extremities without ionizing radiation. It incorporates deep learning reconstruction (DLR) and other AI-powered functions like AutoPose and AutoClip to enhance image quality and improve workflow efficiency, helping radiologists diagnose conditions with improved image clarity and reduced scan times.

Spine Auto Views is a software tool that automatically processes CT images of the spine to create anatomically focused reformatted views and labels vertebrae and disc spaces, facilitating quick and consistent review for clinicians. It utilizes deep learning algorithms to generate multiple reformatted image planes without user interaction and exports the results automatically for radiologist reading.

The Biograph Vision.X and Biograph Vision.X Edge are advanced combined PET and CT scanners that provide high-resolution images by fusing metabolic and anatomical information. These systems assist healthcare professionals in detecting, diagnosing, and monitoring diseases such as cancer, cardiovascular and neurological disorders, and support treatment planning and interventional procedures.

AusculThing ACC is an AI-powered decision support software that analyzes heart sounds recorded by an electronic stethoscope to help healthcare providers distinguish between normal and abnormal heart murmurs. It provides instant, automated analysis and visual and acoustic feedback to support clinical evaluation in both adult and pediatric patients.

MOZI TPS is a radiation therapy planning software that helps clinicians design and optimize treatment plans for patients with malignant or benign diseases. It uses medical images to assist in contouring anatomical structures automatically with deep learning, performs image registration, and calculates radiation dose distribution using Monte Carlo algorithms. This aids clinicians in accurately planning external beam irradiation with photon beams, improving treatment effectiveness and patient safety.