OLYMPUS Winter & Ibe GmbH A37007A Olympus

ConnectWeb viewer front-end with PACSCore back-end archive

icobrain is software intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images.

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.

Looking to sell brand new Endoscope Guide devices by Olympus. They were purchased in 04/2018 for $17,900 and never opened. ScopeGuide UPD-3 is an exclusive Olympus technology designed to provide a real-time 3D image of the shape and position of the endoscope inside the body.

Stryker InstruMed SmartPump Dual Channel Tourniquet System in good working condition. This device offers versatile support for medical procedures involving single cuff, Bier Block, and bilateral procedures on both upper and lower extremities simultaneously. Its Dual Channel system ensures continuous monitoring, control, and real-time display of cuff activities. Additionally, it allows independent management of pressure and inflation time settings for each cuff.

Stryker Tourniquet Dual Channel Pump

ConnectWeb viewer front-end with PACSCore back-end archive

icobrain is software intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images.

Tempus ECG-AF is a cardiovascular machine learning-based notification software intended to analyze recordings of 12-lead ECG devices from patients 65 years of age and older. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed AF in order to evaluate them for referral of further diagnostic follow up and address the unmet need of reducing the number of undiagnosed AF patients.