FDA Radiology AI Devices

The SpotLight / SpotLight Duo with Low Dose Lung Cancer Screening Option is a CT X-ray system that produces cross-sectional images of the body, particularly the cardiovascular and thoracic regions. It includes protocols for low dose CT scans aimed at lung cancer screening, ensuring minimal radiation exposure while maintaining image quality. This device assists clinicians in diagnosing lung cancer and planning related therapies, utilizing advanced image reconstruction algorithms and adaptable scan techniques for patients of various sizes.

Tyto Insights for Rhonchi Detection is an AI-enabled software system that helps healthcare professionals detect abnormal lung sounds known as rhonchi from lung sound recordings. The system processes audio recorded using a compatible FDA-cleared digital stethoscope and provides decision support to clinicians in evaluating lung health, aiding in diagnosis and treatment decisions.

V5med Lung AI is an AI-powered software that helps radiologists detect pulmonary nodules in chest CT images. It automatically analyzes scans and highlights areas with suspected nodules between 4 and 30 mm, acting as a supportive tool to improve accuracy and reduce missed findings. The system integrates easily with clinical imaging workflows and has been clinically validated to improve radiologist performance and reduce reading time.

Rayvolve LN is an AI-powered software that helps radiologists detect and highlight suspected pulmonary nodules on frontal chest X-rays. By serving as a second reader, it aids in reviewing digital radiographs for patients 18 years or older, enhancing diagnostic accuracy without replacing the original chest images.

Rayvolve PTX-PE is an AI-based software tool designed to automatically analyze chest X-ray images for critical findings like pleural effusion and pneumothorax. It helps healthcare professionals prioritize cases by providing study-level notifications for worklist management, thereby improving the efficiency of radiological workflows without replacing clinical decision-making.

The Broncho Videoscope System from Scivita Medical Technology Co. consists of single-use bronchoscopes and endoscopic image processors designed for endoscopic diagnosis and treatment within the respiratory system, including the trachea, bronchi, and lungs. This system provides real-time visualization on monitors, enabling clinicians to observe, diagnose, and treat respiratory conditions effectively in hospital settings.

AVIEW is a software product that assists physicians by providing quantitative analysis of CT images for lung and cardiac evaluation. It offers segmentation of lung structures, characterization and measurement of lung nodules, calcium scoring of coronary arteries, and integrates computer-aided detection tools. It can be used both on-premises and in cloud environments, supporting remote access via mobile devices or browsers. This software enhances diagnostic accuracy and aids in patient management by automating complex image analysis tasks.

The Bronchoscope System by Shenzhen Sanping Image Technology Co. is a single-use flexible bronchoscope designed for use in hospital environments for adult patients. It works with an electronic endoscope imaging processor that captures images inside the airways and tracheobronchial tree using a CMOS image sensor and LED light source, then processes and displays these images on a monitor. This system helps clinicians perform endoscopic diagnosis and treatments safely and effectively with single-use, sterile instruments to reduce infection risks.

ChestView US is an AI software product that analyzes frontal and lateral chest X-rays to detect and mark suspicious areas such as lung nodules, pleural abnormalities, mediastinal/hilar abnormalities, and consolidations. It provides annotated images with highlighted regions to assist clinicians in reading chest radiographs, improving diagnostic accuracy as a concurrent reading aid.

The EFAI Chestsuite XR Malpositioned ETT Assessment System (ETT-XR-100) is software that assists medical professionals by automatically analyzing chest X-ray images to detect misplaced endotracheal tubes. It uses deep learning to flag suspicious cases and prioritize them in the workflow, helping radiologists review these urgent cases more quickly. The software provides alerts when the tube tip is positioned too high, too low, or incorrectly and supports clinical decision-making by improving triage efficiency.