KOROS U.S.A., INC. 7793-04 Dissector

StrokeViewer LVO Triaging is a notification only cloud-based image processing software application using artificial intelligence algorithms to analyze patient imaging data in parallel to the standard of care imaging interpretation.

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.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.

Amplatz Type Renal Dilator/Sheath Set

5 mm Bullet-tipped Endoscopic Dissector Stick

VMA Motion Normalizer (Console, Table, and Upright assemblies)

Fertility lubricant, 40 gram tube with flip top cap, no applicators

VMA Image Acquisition Software (SW1) installed on VMA (Motion Normalizer Console)

StrokeViewer LVO Triaging is a notification only cloud-based image processing software application using artificial intelligence algorithms to analyze patient imaging data in parallel to the standard of care imaging interpretation.

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.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.