FDA Radiology AI Devices

Auto Lung Nodule Detection is a computer-aided detection software designed by Samsung Electronics to assist physicians in identifying and marking suspected pulmonary nodules between 10 and 30 mm on PA chest X-rays. Integrated into Samsung's digital X-ray systems, it serves as a second reader by highlighting regions of interest on images to support radiologists in their interpretation and improve detection accuracy.

The syngo.CT Lung CAD by Siemens Healthcare GmbH is a software tool designed to assist radiologists in detecting solid and subsolid lung nodules on chest CT scans. It uses advanced AI algorithms based on convolutional neural networks to highlight suspicious areas, reducing the chance of missed nodules. The software integrates with existing medical imaging platforms, improving clinicians' efficiency and accuracy in lung nodule detection, a critical task in lung cancer screening and diagnosis.

Optellum Virtual Nodule Clinic is an AI-powered software that helps medical professionals track, assess, and characterize pulmonary nodules detected incidentally in CT scans. It provides a malignancy likelihood score using a convolutional neural network to support clinical decision making, improving diagnostic accuracy and patient management workflows for lung nodules.

NinesMeasure is a software tool designed to help trained radiologists measure the size of pulmonary nodules on adult thoracic CT scans. It uses artificial intelligence to automatically calculate long and short axis diameters of nodules, providing quantitative information for better assessment and tracking over time. This tool assists clinicians by streamlining nodule measurement, though it is not intended to replace full patient evaluation or diagnosis.

AView LCS by Coreline Soft Co., Ltd. is a medical software tool that helps clinicians review, analyze, and report on thoracic CT scans. It automatically detects and measures lung nodules, calculates malignancy risk, supports follow-ups with nodule matching, and generates diagnostic reports. This improves the accuracy and efficiency of lung nodule assessment and lung cancer screening.

AVIEW by Coreline Soft is a comprehensive medical imaging software that processes CT images of the lungs and heart. It helps physicians analyze lung tissue quantitatively, characterize lung nodules, evaluate coronary artery calcium, and monitor progression of lung disease. The software provides advanced segmentation, registration, quantitative metrics, and reporting tools, integrating AI techniques such as deep learning and CAD to enhance diagnostic support and workflow efficiency.

InferRead Lung CT.AI is software that helps radiologists detect lung nodules in chest CT scans. It uses AI algorithms to identify and mark nodules, providing measurements and characterization to aid diagnosis, specifically targeting an asymptomatic population. The software integrates with existing PACS systems and assists clinicians by enhancing nodule detection sensitivity and reducing interpretation times.

AVIEW LCS by Coreline Soft Co., Ltd. is a diagnostic software tool that reviews, analyzes, and reports on thoracic CT images to help clinicians characterize lung nodules. It automatically measures nodule size, volume, density, and growth over time, integrates with FDA-cleared CAD for nodule detection, and provides lung cancer risk scoring and standardized Lung-RADS categorization. This helps clinicians detect, monitor, and manage lung nodules more accurately and efficiently.

syngo.CT Lung CAD is an AI-powered software tool that helps radiologists detect solid pulmonary nodules in chest CT scans. Acting as a second reader, it alerts clinicians to potential nodules that might have been overlooked during their initial review, thereby improving diagnostic accuracy and patient care.

ClearRead CT is a software application that helps radiologists detect lung nodules in chest CT scans by providing enhanced image processing with vessel suppression and computer-aided marking. This assists radiologists in identifying potential nodules more effectively and efficiently during their review, improving diagnostic accuracy and workflow.