NEAT 001 is a software medical device that automatically analyzes EEG data obtained during sleep studies to detect different stages of sleep according to established sleep medicine guidelines. It helps clinicians by providing automated scoring and visualization of sleep stages, improving the efficiency and consistency of sleep evaluations in adult patients.
The EPIQ Series and Affiniti Series Diagnostic Ultrasound Systems by Philips Ultrasound LLC are advanced diagnostic ultrasound imaging systems used in various clinical settings, including hospitals and clinics. They provide imaging and fluid flow analysis of multiple body regions through modes like B Mode, Doppler, and Harmonic Imaging, helping clinicians diagnose a wide range of conditions. A new Remote Software Management feature enhances workflow by allowing remote software upgrades and maintenance, improving system availability and usability without hardware changes.
syngo.CT LVO Detection is an AI-powered post-processing software that analyzes CT angiography images of the brain to help clinicians prioritize cases by detecting suspected large vessel occlusions in specific brain vessels. It aids in triage but is not used for standalone diagnosis or altering images, thereby assisting medical professionals in managing stroke patients more efficiently.
Vitrea CT VScore is a software application designed to aid medical professionals in visualizing, segmenting, and quantifying calcified lesions in ECG-gated cardiac CT images of patients aged 30 years or older. It provides calcium scoring and generates detailed reports to assist clinicians in cardiac imaging assessment.
autoSCORE V2.0 is an AI-based software that helps medical professionals review EEG recordings by identifying and classifying sections indicating brain abnormalities such as epileptiform and non-epileptiform activities. It assists neurologists by marking likely abnormal EEG segments, providing probabilities for specific abnormality types, thereby enhancing the efficiency and accuracy of EEG interpretation without replacing clinical judgment.
BunkerHill BMD is an AI-based software tool designed to estimate bone mineral density from existing CT scans of the spine in adults aged 30 and over. It helps clinicians identify low bone density without requiring dedicated bone densitometry scans, supporting retrospective assessment and aiding clinical decision-making.
Horos Mobile is a mobile software application that allows trained healthcare professionals to view and review medical images from CT, MRI, X-ray, ultrasound and other DICOM compliant systems on iOS and iPadOS devices. It facilitates diagnostic image visualization remotely or when full workstations are not available, supporting essential tools for image manipulation and measurement consistent with clinical practice requirements.
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.
DeepFoqus-Accelerate is an AI-driven software solution designed to improve brain MRI scans by reconstructing accelerated MRI data up to 4 times faster, producing clinical-quality images from undersampled data. It helps radiologists and technologists by enhancing non-contrast brain MRI images from 1.5T or 3T Siemens and GE scanners, making MRI scanning more efficient while maintaining image quality.
The EyeBOX EBX-4.1 is an eye-tracking device designed to help diagnose concussions by measuring eye movements. It uses a high-speed infrared camera to record gaze while the patient watches a video stimulus. The device automatically analyzes the data and produces a BOX score indicating the likelihood of concussion, comparing the eye tracking metrics to a normative database of uninjured individuals. This helps clinicians assess brain injury more objectively and quickly.
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