FDA Radiology AI Devices

AutoContour RADAC V2 is a software tool that helps radiation treatment planners by automatically outlining anatomical structures in CT and MR images to assist in radiation therapy treatment planning. It uses machine learning models trained on large datasets to generate these contours and allows clinicians to review and modify them before exporting for use in treatment.

MVision AI Segmentation is a software device designed to automatically create segmentation templates of anatomical regions on CT images to assist clinicians in radiation therapy treatment planning. It uses machine learning-based algorithms to generate initial contours for multiple anatomical sites, which clinicians can review and modify before use, helping to save time and improve efficiency in clinical workflows.

The EFAI RTSuite CT HN-Segmentation System is AI-powered software designed to assist radiation oncology professionals by automatically contouring organs at risk in head and neck CT images. It helps streamline radiation therapy treatment planning by providing initial segmentations that clinicians review and edit, improving workflow efficiency and accuracy.

RT-Mind-AI is a software tool designed to automatically segment organs-at-risk in non-contrast CT images for patients undergoing radiation therapy. It helps radiation oncologists by providing accurate anatomical contours that support treatment planning and evaluation, improving workflow and precision.

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.

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.

Automatic Anatomy Recognition (AAR) is a software medical device that uses deep learning to automatically identify and contour anatomical structures from CT scans. It is designed to assist technicians and physicians in radiation therapy planning by providing precise contours of organs at risk in the head, neck, and chest areas. This helps improve the accuracy and efficiency of treatment planning for patients undergoing radiation therapy for cancers in these body regions.

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.

ART-Plan is a medical software tool that helps clinicians plan radiation therapy for cancer patients. It supports image registration and fusion of CT, PET, and MRI scans to better visualize tumor and healthy tissues. It offers automatic, semi-automatic, and manual contouring of organs at risk and lymph nodes, saving time and improving precision in treatment planning. The software is meant for use by trained professionals in clinical settings.

AI-Rad Companion Organs RT by Siemens is a post-processing AI software that automatically contours organs at risk on CT images to aid radiation therapy treatment planning. It uses deep learning algorithms to generate organ contours that can be reviewed and accepted by trained medical professionals, helping streamline radiotherapy preparation and ensure accurate targeting of treatment.