AI for Healthcare

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

In this lesson, you will be given an introduction to this course about AI for 2D medical imaging, why AI is important and where AI fits in the space.

In this lesson, we will cover clinical foundations such as clinical workflows, applications of 2D imaging in clinical settings and how machine learning impacts clinics.

In this lesson, we will learn the DICOM standard in medical imaging, and how to explore medical imaging data and prepare it for machine learning applications.

In this lesson, we'll dive deep into classification tasks for 2D medical imaging using different machine learning models. and we will talk about pre-process data, train, test, and validate models.

In this lesson, you will learn about how your work fits into the bigger picture, and how it’s regulated by the FDA, which is an often-overlooked, but incredibly important.

In this lesson, we will introduce the course and instructors. We will give you an overview of the context for AI in 3D medical imaging space, and cover the objectives of the course.

In this lesson, we cover the basic terminology and concepts related to 3D medical imaging. We will look at the problem space from a clinical standpoint and learn how CT and MR scanners produce images.

In this lesson, we will dive deeper into medical imaging formats NIFTI and DICOM, how scanner data is represented, and how to read medical volumes stored in these files and analyze them.

In this lesson, we cover the basics of building deep neural networks for 3D medical imaging (mostly segmentation & classification) and performance evaluation from a software and clinical perspective.

In this lesson, we'll talk about clinical networks, architecture, and AI deployment, tools and their use by data scientists and clinicians, as well as medical device regulation and data privacy.

This lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.

In this lesson, you will learn about the importance of data security and the different standards that apply to EHR, as well as analyzing EHR data.

In this lesson you will learn how to work with different EHR codes and how to map them properly to records.

In this lesson, you'll gain skills in feature engineering and transformation of EHR.

In this final lesson, you'll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty.

We’ll cover what wearables are and the scope of the class. Learn who your instructor is and his thoughts on the promise and caveats of wearables in medical research and decision making.

A brief tour through sampling theory, signal processing, the Fourier transform, and other related topics. We’ll briefly cover some plotting and visualization techniques here as well.

We cover the basics of the accelerometer, the PPG sensor, and the ECG sensor, as well as what these signals look like in typical environments and the types of noise that we will encounter.

Build an activity classifier using a wrist-worn accelerometer!

We deep dive into a fundamental algorithm for ECG processing and use that as the basis for an arrhythmia detection algorithm.