Medical Physics Games

Medical Physics Games in Education

I had the wonderful opportunity to learn about educational tools and strategies from Cornell's Center for Teaching Innovation. One topic of interest was diving into some of the challenges educators face for those in professional tracks, such as medical residents, interns, and students as they follow their educational trajectories into professions. Being involved in Medical Physics training and education for quite some time, I have come to appreciate the challenges Medical Physics graduate students and residents often face, as time to dig deep into content required for competency in their practice must be balanced with the time devoted in completing their projects or clinical rotations. 

Tools like flipping the classroom, digital media and resources, and other strategies which help create a safe learning environment heavily influence how well learners retain knowledge. Games can be a powerful tool to reinforce knowledge retention for youth. More recently, there has been a surge of research delving into how gaming could be deployed in medical education. 

There are a tonne of great education resources for Medical Physics resources (in fact, there are perhaps too many... which gives me a point of entry into another project aimed and linking that content with learning objectives as defined in the IAEA syllabus for Radiation Oncology Medical Physics education). But, there were not a lot of 'fun' educational activities targeted in Medical Physics. So that is where this journey begins!

I've started compiling a list of medical physics educational games as well devoting some energy to make some. Below is a growing list. If you have some to share, please e-mail me your suggestions!

Radiation Oncology Game - Intended Learners

Radiation Oncology Residents (Human and Veterinary)

Diagnostic Imaging Residents  (Human and Veterinary)

Medical Physics Graduate Students

Medical Physics Residents

Individual or groups

https://jeopardylabs.com/126211

Jeopardy - Radiation Oncology Medical Physics - Basics 1

Jeopardy - Radiation Oncology Medical Physics - Basics 2

Jeopardy - Radiation Oncology Medical Physics - Hard

Jeopardy - Radiation Oncology Treatment Planning - 1

Jeopardy - Radiation Oncology Treatment Planning - 2

Jeopardy - Radiobiology 1

(more to come)

Groups

Family Feud - Radiation Oncology Medical Physics (X-ray interactions/sources, radioisotopes)

Counterfactual learning systems

Separating causation and correlation in AI systems is a challenge because most machine learning systems look for trends, but not 'counterfactual' information, which is more like the way we, humans, and doctors think.

People, like doctors, make decision based on what they know to be true and untrue, and build causal reasoning into a diagnosis. Most #machinelearning systems don't build causality: they are built on associations / correlations. We don't care the sky is probably blue when we get a cold, but we do care your T-cell count is low when you get a cold (causation vs. correlation). Counterfactual data? "Lets get a chest x-ray / ultrasound / CBC ..." i.e., some data that rules out other possibilities to see how symptom relates to disorder (directly or indirectly). But what rules can you build for machine learning? (Un)surprisingly this paper shows it can be simple, (because thats how *our* brains probably work): disease should be consistent with diagnosis, rule out stuff that isn't possible, and keep it simple: 1 Dx fitting M symptoms is better than N Dx fitting M symptoms. They go on to define things called "expected disablement" and "expected sufficiency". The former is obvious, but the latter is like "sufficient cause", and state theorems, one of which is that disablement and sufficiency are sufficient conditions for the rules above. But real data is noisy and murks the variables and so there needs to be a way to account for noise. (insert mathy stuff here). Thats all fine, but the litmus test is "How does this compare to actual clinical decisions?" In short, a physician achieves higher accuracy in diagnosing a disorder for simpler problems, and the algorithm outperforms for more complex problems. Thats good for rare disease classification. That makes sense as the story of #machinelearning and #AI in medical diagnoses suggests utility in a role as a 'decision support tool', but not a fully autonomous one. The difference here is that the model behaves more like a clinician would. For you Bayesians ... when you first learned Bayes' Theorem I bet you pondered "Why can't we do counterfactual inference in medical diagnosis? ...policy making? ... court decisions?". This article is a nice progression of how we can use AI based on causation - not just correlation. Don't believe me? Read for yourself.

https://www.nature.com/articles/s41467-020-17419-7?fbclid=IwAR3a6txrtrUtr6IDyaV-Bsc56bbUJmHfGtg3RVoyhMj0LvR-wvuRTm7A7YU

Extracellular vesicle and particle biomarkers and AI

A very interesting article on extracellular vesicle and particle biomarkers and how they might be used in cancer detection.

 

https://www.sciencedirect.com/science/article/pii/S0092867420308746?via%3Dihub

There are gazillion authors from a bajillion institutions on this paper. Collaboration!

The gold standard to confirm cancer and other aliments is a tissue biopsy, where a small sample of tissue is extracted from the suspicious growth. But extracting a tissue sample isn’t possible in many situations, especially when there are other co-morbidities where the biopsy can introduce more problems than it attempts to solve.

So ‘liquid’ biopsies is another approach: stuff like drawing blood, lymphatic/bile, etc., which is not as difficult. But that stuff isn’t where the tumor is… its stuff floating around the body. Some of the gunk that floats around outside the cell are EVPs...or ‘extracellular vesicles and particles’. Basically they’re goops of stuff that float outside the cell, originating from ‘sorters of things’ in your cells. I (probably mistakenly) think of them as recipe pages floating outside the bookstore that sells recipe books. Except there are gazillion (actually billions of EVPs) recipes, and a gazillion books: trying to figure out what page came from what book would seem an impossible task, right? Well… this is where the story gets interesting!

This team used machine learning techniques to sort through all the EVPs based on sizes and other subcategories (mice/human, cancers). They found that the relationship between +10K EVPs and tumors in mice and humans were not the same (interesting since mouse models are used in so much research). They then sifted through all these possible markers to see if they could be used as a cancer detector.

How do you sort through literally 10s of thousands of markers for trends? Reliably? #Machinelearning, of course. They found the presence/absence of 13 common EVPs could be used to classify both lung and pancreatic cancers. But are those little floaters actually associated with tumors? In other words, is there a relationship between biopsy findings and the floaters?

While their dataset was kinda small, they could verify the biopsy findings with the floaters to +90% sensitivity / specificity (sensitivity is how well you can detect something (like how likely you are to stop at a sign that looks like a stop sign), and specificity is how well you can rule all other possibilities out (like how well you ignore the sign that looks like a stop sign but really isn’t). They then attempted to ensure that what they saw wasn’t just stuff you’d seen normally... not a trivial task.

What does it all mean? Maybe *earlier* cancer detection? Increased precision cancer detection? Dunno… but it is super cool that floaters in the blood could be so precise in detecting disease. These EVPs may be echoes of the body saying ‘something ain’t right’. We didn’t have the tools to be able to appreciate this signal until we developed the technology to detect the echoes.

Super cool.

Meet RoboBEER

Meet RoboBEER, a robotic beer pourer.

As you know, the demand for high quality beers worldwide has exploded over the last few decades. What drives quality? Well one way to discern quality is to objectively characterize features within the beer.

What features you may ask? Some of them are visual, like the color and foam-ability, such as maximum volume of foam, total lifetime of foam, foam drainage, size of the bubbles in the foam. But not just any idiot can pour the beer, as a Guinness lover will tell you, since a good pour is crucial. Fortunately RoboBEER can pull the ‘perfect’ pint: RoboBEER pulls 80 mL (+/- 10 mL) while monitoring the liquid temperature, assessing the alcohol and CO2 levels, all through your kids Arduino control board and a Matlab interface (yeay Matlab!).  

But what about more important features like taste? Surely no robot could do that right? No way. But… maybe you could predict things like mouthfeel from all the features obtained in by RoboBEER? You could capture descriptions of taste from experts through a questionnaire: 10 basic categories: bitter, sweet, sour, aroma in grains, aroma in hops, aroma in yeast, viscosity, astringency, carbonation mouthfeel, and flavor hops. Then, have them sample twenty-two beers. (What I would do to be a part of this study!)

Could you train a neural network to predict what the beer would taste like just based off the data from RoboBEER?

A ‘feedforward’ neural network was designed where, essentially, you take all the inputs from the RoboBEER (head size, color, etc), and the outputs from the tasters (bitterness, sweetness, mouthfeel) and see if a neural network can predict the taste based on those inputs. You do some fun math like principal component analysis to help with sorting all the data and patterns, pump them into the network for AI training and what do you get?

For the independent testing data, the AI system from RoboBEER data could predict what it a beer would taste like with an accuracy of 86%. What does this mean? Well… very likely, RoboBEER is a better judge of beer than you are. And it doesn’t even have to taste the beer.

Don’t believe me? Read for yourself.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.14114