FDA Radiology AI Devices

EchoGo Heart Failure 2.0 is an AI-powered software tool that analyzes echocardiogram images of the heart's apical four-chamber view to support clinicians in detecting heart failure with preserved ejection fraction (HFpEF). It provides a diagnostic aid by outputting a classification and a confidence score, enhancing cardiovascular assessments and helping guide clinical decision-making.

The syngo.via MI Workflows, including Scenium and syngo MBF, is a multi-modality software platform developed to assist healthcare professionals in viewing, manipulating, quantifying, and analyzing medical images from various imaging modalities, including PET, SPECT, CT, and MR. It supports disease management in oncology, cardiology, neurology, and organ function, and aids physicians in radiotherapy treatment planning by harmonizing PET data and providing standardized analysis tools such as brain amyloid quantification.

AISAP Cardio V1.0 is an AI-powered software that analyzes cardiac point-of-care ultrasound images to detect and assess valvular pathologies like regurgitations and stenosis, and measures key cardiac functions such as ejection fraction and chamber sizes. It supports physicians by generating diagnostic reports that aid in interpreting ultrasound images, improving accuracy and efficiency in evaluating heart conditions in adult patients.

The Huxley SANSA Home Sleep Apnea Test is a wearable device that records various physiological signals such as oxygen saturation, heart rate, chest movement, snoring, and sleep stages. It uses AI/ML algorithms in a cloud-based platform to analyze these signals and assist healthcare professionals in evaluating sleep-related breathing disorders like sleep apnea, both in clinical and home settings.

Tyto Insights for Crackles Detection is an AI-powered software that analyzes lung sound recordings made by a compatible stethoscope to detect abnormal lung sounds called crackles. It supports clinicians by providing automated detection to help evaluate lung health in both adults and children aged two years and older. The device aids healthcare providers in clinical decision-making by highlighting recordings suggestive of crackles, which may indicate underlying lung conditions.

Tempus ECG-AF is an AI-powered software that analyzes standard 12-lead ECG recordings from patients aged 65 and older to detect signs indicating an increased risk of atrial fibrillation or atrial flutter within the next 12 months. It assists clinicians by providing risk notifications based on ECG data, improving early identification of patients who may require further diagnostic follow-up.

Corvair is an AI-powered software that analyzes resting ECG recordings to detect various heart rhythms, morphological abnormalities, and measure ECG intervals. It supports healthcare professionals by providing an initial automated interpretation of ECGs to assist in cardiac diagnosis, enhancing accuracy and efficiency in clinical settings.

The EFAI Bonesuite XR Bone Age Pro Assessment System (BAP-XR-100) is software that uses deep learning to analyze left-hand X-ray images in pediatric patients aged 2 to 16 years. It estimates bone age to assist pediatric radiologists in clinical decision-making. The system integrates with existing PACS workflows, providing bone age assessments to support but not replace radiologist interpretation.

LVivo IQS is an AI-powered platform designed to analyze ultrasound images of the heart, specifically providing real-time quality scoring of right ventricle images from the 4-chamber apical view. This software helps clinicians by automatically detecting, measuring, and calculating important cardiac parameters and providing feedback on image quality, enhancing the reliability and interpretability of ultrasound examinations.

The AEYE-DS device helps healthcare providers automatically detect more than mild diabetic retinopathy in adults with diabetes by analyzing retinal images taken by specific fundus cameras. It uses AI technology to analyze the images and provides diagnostic screening results, improving early detection and management of diabetic eye disease.