DAIM Seminar 1 Introduction

Let’s Do Digital Team

Introduction

• Welcome to the first seminar of the Data Analytics in Medicine (DAIM) - Images course!
• Today, we will be discussing:
  • The clinical benefits of learning to work with images
  • Programming resources that you can use throughout the course

Why programming in medicine?

• The global market for AI in medicine has grown from
  • $0.2 billion in 2015 to $13.7 billion in 2023
• “The usefulness of human–AI collaboration will likely depend on the specifics of the task and the clinical context.” - “AI in health and medicine”, in Nature
• Clinicians will be, and should be, leading the deployment of these technologies

Why programming in medicine?

• There is risk of harm to patients if technology is deployed incorrectly or inappropropriately
  • “…almost half recorded instances of potential patient harm linked to their systems.”
• Involving doctors in development helps idea sharing and helps us advocate for patients.

Why programming in medicine?

• Most translational bioengineering or informatics research will use some degree of programming in Python, MatLab, or R
• This skills allows for some degree of work across clinical settings and academia

5-minute open discussion

• What is the top reason that doctors should learn to program?
• What are the disadvantages of programming for doctors?

Break!

Python Environments

• We will be using Google Colab for the workshops of DAIM - Images.
• Google Colab uses Jupyter Notebooks to run Python code
  • Easy to set up
  • Easy to use
  • Powerful
• Many other biomedical coding resources use this environment, e.g. Kaggle, AlphaFold

Google Colab Window

TODO Use marked-up screenshot of first workshop

Using Google Colab

• A notebook consists of cells
  • Code cells
  • Markdown cells
• When you run the first line of code, the kernel starts
• Variables persist between cells:

Trying out Google Colab

• We would recommend testing out Google Colab before the first workshop.
• The default notebook from Google can be found here.

Resources to use

• There are multiple resources that programmers often use when trying to solve a particular problem
  • Code documentation - reliable and accurate
  • StackOverflow - real-world solutions with explanations
  • Github - Talk with the developers

Code documentation

• Documentation contains information about how to use the code in a library.
• It should be the first place you go if you don’t understand how to use a function/class.
• Contains information about arguments and use cases.
• Search for the function (e.g. numpy.shape()) followed by “docs”.

Reading Documentation

The documentation for NumPy’s ‘shape’ method, a very commonly used NumPy method

StackOverflow

• StackOverflow is a website created in 2008 to allow programmers to share programming issues.
• There are other websites (Stack Exchanges) for other fields that have been created in the same format.
  • Medical Sciences Stack Exchange

A StackOverflow Window

A StackOverflow question about the yield keyword in Python.

Github

• A site where code is developed and hosted
• Built around Git repositories
  • Git is a useful tool to know about
• Many answers can be found there by:
  • Looking at source code
  • Github issues

Github Code File

A GitHub code file for a simple CT segmentation algorithm.

Github Issues

A GitHub issue on the NumPy codebase.

Writing Python

• As you start to write more complex code, readability is vital
• In general, you should aim to use one of the following when naming variables, etc.
  • Snake-casing e.g. data_analytics_in_medicine
  • Camel-casing e.g. dataAnalyticsInMedicine
• More style guidelines can be found here (PEP 8)

Break and Questions!

Clinical Considerations

• Some data is easier for computers to process than others.
• How can we make clinical data easier to work with?

Clinical Considerations

• Standardised data formats
  • This allows for interoperatibility between systems and facilitates research
  • DICOM is a successful example of this.
  • We will be discussing DICOM in Module 2 of this course.

Clinical Considerations

• Structured data
  • Labelling data consistently
  • Human-labelled data is often variable
  • Use an encoding in your data collection protocol to make processing easier (e.g. “CXR” = 1)

scan = "CXR"
# "chest X-ray", "chestXR", "cxr", "chest film", etc.

5-minute open discussion

• Can you think of other considerations that can make clinical data easier to process?
• Have you ever encountered any issues with this in your practice?

Thank you!

Any questions?