The use of Artificial Intelligence (AI) techniques is growing in the medical and healthcare industry such as radiology diagnostics, digital health, physiological signal analysis, and patient monitoring applications. These powerful techniques can be applied to signals as well as images to perform tasks such as segmentation, classification and prediction.
MathWorks developers have purpose-built MATLAB's machine and deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing an AI model. Ultimately, models need to be incorporated into a medical device or a cloud platform to deliver a final product or a service to the market.
In this technical presentation, we will explore the workflows involved in developing and deploying AI-based algorithms for medical applications. We will use real-world EMG and ECG datasets to demonstrate different machine learning and deep learning approaches to classify and predict 1-D time-series signals. We will also use these techniques for medical image classification and segmentation using clinical case studies such as left ventricle (LV) segmentation from cardiac MRI images and classification of parasitology slide images.