Course 1: Welcome to the AI for Healthcare Nanodegree Program 1 hourAI for Healthcare Nanodegree Program IntroductionGetting HelpYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.Course 2: Applying AI to 2D Medical Imaging Data 35 hoursIntroduction to AI for 2D Medical ImagingIn 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.Clinical Foundations of 2D Medical ImagingIn this lesson, we will cover clinical foundations such as clinical workflows, applications of 2D imaging in clinical settings and how machine learning impacts clinics.2D Medical Imaging Exploratory Data AnalysisIn 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. Classification Models of 2D Medical ImagesIn 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.Translating AI Algorithms for Clinical Settings with the FDAIn 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.Project: Pneumonia Detection from Chest X-RaysCourse 3: Applying AI to 3D Medical Imaging Data 25 hoursIntroduction to AI for 3D Medical Imaging 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.3D Medical Imaging - Clinical FundamentalsIn 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.3D Imaging Exploratory Data AnalysisIn 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. 3D Medical Imaging - End-to-End Deep Learning ApplicationsIn 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.Deploying AI Algorithms in Real World ScenariosIn 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.Hippocampal Volume Quantification in Alzheimer's ProgressionIn this project, you will curate a dataset of brain MRIs, train a segmentation on a CNN, and integrate this into a clinical network to quantify hippocampal volume for Alzheimer's progression. Course 4: Applying AI to EHR Data With the transition to electronic health records (EHR) over the last decade, the amount of EHR data has increased exponentially, providing an incredible opportunity to unlock this data with AI to benefit the healthcare system. Learn the fundamental skills of working with EHR data in order to build and evaluate compliant, interpretable machine learning models that account for bias and uncertainty using cutting-edge libraries and tools including Tensorflow Probability, Aequitas, and Shapley. Understand the implications of key data privacy and security standards in healthcare. Apply industry code sets, transform datasets at different EHR data levels, and use Tensorflow to engineer features. 14 hoursApplying AI to EHR Data IntroductionThis lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.EHR Data Security and AnalysisIn 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.EHR Code SetsIn this lesson you will learn how to work with different EHR codes and how to map them properly to records.EHR Transformations & Feature Engineering In this lesson, you'll gain skills in feature engineering and transformation of EHR.Building, Evaluating and Interpreting ModelsIn this final lesson, you'll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty. Project: Patient Selection for Diabetes Drug TestingIn this project students will use what they learn in the classroom to apply AI in healthcare for patient data.Course 5: Applying AI to Wearable Device Data 16 hoursIntroduction to Wearable DataWe’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.Intro to Digital Sampling & Signal ProcessingA 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.Introduction to SensorsWe 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.Activity ClassificationBuild an activity classifier using a wrist-worn accelerometer!ECG Signal ProcessingWe deep dive into a fundamental algorithm for ECG processing and use that as the basis for an arrhythmia detection algorithm.Motion Compensated Pulse Rate EstimationIn this project, you will create a pulse rate algorithm that takes into account activity and apply this algorithm to a new data set to determine clinically significant features. CompanyAbout Us Why Udacity? 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