FDA Radiology AI Devices

FEops HEARTguide™ ALPACA is an AI-enabled medical imaging software system that helps clinicians visualize and measure detailed heart and vessel structures from CT images. It aids in planning minimally invasive heart procedures by providing automated segmentation, 3D visualization, measuring tools, and reporting. This software supports better procedural decisions by combining anatomical insights with clinical evaluation.

Autoplaque 3.0 is a post-processing software designed for use with coronary CT angiography images. It helps cardiologists and radiologists analyze coronary arteries to detect and measure plaques and stenosis. The AI-driven software automates vessel, plaque, and lumen segmentation with an option for clinician review, enabling faster and more standardized cardiac image analysis.

The Radiation Planning Assistant (RPA) is a web-based software tool designed to automate and assist in planning radiation therapy for cancers of the head and neck, cervix, breast, and brain metastases. It uses CT images to create contours and treatment plans with photon beam irradiation, improving efficiency and precision in radiotherapy treatment planning. The system generates outlines of organs and tumors and proposes radiation dose plans, which clinicians then review and adjust in their own treatment planning systems before use.

Auto Segmentation by GE Medical Systems is an AI software tool that automatically generates organ-at-risk contours from CT scans to help radiation oncologists, medical physicists, and dosimetrists speed up radiation therapy planning. It produces initial segmentation contours that users can review and adjust, improving workflow efficiency and precision in radiation treatment.

The iCAC Device is a software tool that analyzes routine chest CT scans to automatically detect and quantify coronary artery calcium, which helps physicians assess cardiovascular risk. It provides outputs such as calcium segmentation visualizations and quantitative calcium scores during standard clinical workflows, assisting physicians without replacing original reports or scans.

Deep Learning Image Reconstruction by GE Healthcare Japan Corporation is a deep learning based software integrated into CT scanners to reconstruct high-quality cross-sectional images of the head, whole body, cardiac, and vascular systems. It uses a trained deep neural network to reduce image noise and artifacts while maintaining spatial resolution, aiding clinicians in obtaining clearer diagnostic images with routine CT throughput.

AutoContour Model RADAC V3 is an AI-enabled software designed to automatically contour anatomical structures in CT and MR images to assist radiation treatment planners in preparing radiation therapy plans more efficiently. It uses deep learning models to generate contours of organs and tumors, allowing clinicians to review and adjust as needed, which streamlines the treatment planning process and improves accuracy.

CerebralGo Plus is a software package designed for trained medical professionals to view and process CT Angiography images of the head and neck. It helps visualize large vessels from adult CTA scans to support clinical decisions, although it is not intended for primary diagnostic use. The software integrates with standard DICOM-compliant imaging devices and hospital systems to facilitate image management and processing.

Limbus Contour is a software tool that automatically segments CT and MRI images to help radiation oncologists, dosimetrists, and medical physicists create precise contours of healthy anatomical structures. These contours are used as inputs for radiation treatment planning, improving accuracy and efficiency in therapy. The software integrates with existing treatment planning systems and image viewers, assisting in contour creation, modification, and management without displaying images directly.

Contour ProtégéAI is a medical imaging software accessory designed to assist trained medical professionals by automatically creating anatomical contours on CT and MR images using machine learning. It helps in radiation therapy planning by segmenting various body structures, including the prostate and pelvic organs, enabling quantitative analysis, adaptive therapy, and patient follow-up. Results generated by the AI can be reviewed and edited to ensure accuracy.