Solution Overview

Patient Selection Algorithm Development for Cardiac Resynchronization Therapy

Cardiac Resynchronization Therapy (CRT) can be a life-saving therapeutic option. However, the intervention itself has considerable risks, carries significant expense and its benefits to heart failure patients are limited to a small group of suitable patients, making patient selection critical. The right algorithm can determine patient selection criteria to identify patients most likely to benefit from CRT and least likely to suffer pre-operative or post-operative complications.

Detecting MRI artifacts Using Deep Convolutional Neural Networks

Magnetic resonance imaging (MRI) artifacts, distortions or false signals that affect image quality, may adversely affect diagnostic quality, resulting in potential diagnostic errors and the need for costly and time consuming repeat examinations that may delay timely treatment. Detecting these artifacts with convolutional neural networks can save costs and improve outcomes.

This website uses cookies

To provide you with the best possible experience on our website, we may use cookies, as described here. By clicking accept, closing this banner, or continuing to browse our websites, you consent to the use of such cookies.

I agree