Anspach Effort, The VIO-3 null

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QuantX is a software program that is used to view and analyze patient breast 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.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.

TOOL MR8-26MH30 MR8 26CM MATCH 3MM DIAM

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ConnectWeb viewer front-end with PACSCore back-end archive

ARYS me is intended for over-the-counter use for self-directed management of arm rehabilitation without specific treatment or treatment recommendations.

Radiance V3 is a software system intended for treatment planning and analysis of radiation therapy administered with devices suitable for intraoperative radiotherapy.The treatment plans provide treatment unit set-up parameters and estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by the intended user.The system functionality can be configured based on user needs.The intended users of Radiance V3 shall be clinically qualified radiation therapy staff trained in using the system.

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

QuantX is a software program that is used to view and analyze patient breast 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.

FeetMe Evaluation mobileAndroid application version 3.Y.Z.