Rayvolve AZchest is AI-powered software that analyzes chest X-rays to help radiologists detect and highlight key chest abnormalities like pleural effusion, pneumothorax, consolidations, and increased heart size. It uses advanced deep learning algorithms to mark areas of concern, providing extra information to aid diagnosis and workflow, but does not replace the clinician's judgment.
Intended Use
Rayvolve AZchest is a radiological computer-assisted detection and diagnosis (CADe/x) software device that analyzes frontal chest radiographs for the presence of specific targeted pathologies within the Pleura, Mediastinum, and Parenchyma.
Technology
The device uses deep learning techniques to detect, identify, and provide diagnostic outputs by labeling regions of interest (ROIs) on chest X-rays, highlighting pleural effusion, consolidation, increased cardiothoracic ratio, and pneumothorax with bounding boxes or contours. It integrates with DICOM node servers, is designed to work with PACS systems, and runs on cloud or on-premise platforms.
Performance
Performance was validated through software verification and validation, standalone bench testing, and a multi-reader, multi-case (MRMC) clinical study testing detection of chest abnormalities. Standalone assessment showed high sensitivity, specificity, and AUC for all targeted pathologies. MRMC studies demonstrated improved diagnostic accuracy (AUC), sensitivity, and specificity for readers using Rayvolve AZchest compared to unaided reads. Workflow improvements, such as reduced reading time, were also demonstrated.
Predicate Devices
No predicate devices specified
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