ESP8266 is a wifi enabled microcontroller. One of the most helpful ones because of it’s wifi ability and very low cost. This makes the ESP8266 popular in even commercial products that need wifi connectivity.
For development purposes, there are also a lot of variants for this chip. After some preliminary research, there appears to be two most helpful breakout boards for it.
NodeMCU is technically the name of the Lua-compatible firmware for ESP8266, which later added support for ESP32 (the more powerful, dual-core sibling of ESP8266). NodeMCU was created in 2014 when user Hong committed the first file of nodemcu-firmware to GitHub. but people sometimes use this term to refer to breakout boards using ESP8266 following this particular schema. It comes with additional chips that enable USB-to-serial and other “quality of life” enhancements that make development easier. The breakout board is also compatible with solderless breadboards, making prototyping much easier.
In a recent Harvard Business Review article, Ajay Agrawal and coauthors shared a simple tool to think about how an AI tool may be deployed. Although the tool is no more complex than 6 boxes of free text, it does follow a number of best practices when thinking about general data and machine learning:
Always define an end-goal – what’s the desired outcome?
You should always make a hypothesis of what may drive this desired outcome.
You should determine how to present the ML prediction in a way that drives action, not just the data itself.
Your data acquisition strategy should include a feedback mechanism.
For example, this is how one might fill out the AI Canvas tool in a radiology use case:
Prediction: Predict whether a brian MRI for a cancer patient contains increasing or new hydrocephalus
Action: Label the examination as critical, and denote that AI has determined a critical finding. For example, create an “AI-STAT” category on worklist priority.
Judgment: Compare the cost of interpreting this brain MRI at its usual turnaround time, versus immediately.
Outcome: Observe whether the action taken in response to a study labeled AI-STAT is correct.
Input: New MRIs of the brain MRI performed, and their prior studies.
Training: Historical brain MRIs
Feedback: Identify false positives – perhaps the prior study was from 20 years ago, or there’s been surgical resection, so that ex vacuo dilation of ventricles is not hydrocephalus. Perhaps there has been recent surgery Identify false negatives – subtle enlargement of the temporal horns missed by AI. Use this information to improve the AI.
How might you use this worksheet to brainstorm AI for your radiology practice?
There’s an origin story for every superhero; even those without superpowers (like Batman – that’s right) got started somewhere. What we sometimes forget is that there is also an origin story for every regular person, every profession, every hobby.
Source: Wikimedia Commons
If you’re a radiologist looking to learn a few things in radiology data science, a simple web search will reveal a seemingly overwhelming amount of material you might have to know.
Fortunately, only a very small subset is necessary to start being productive. Here are a few resources I used to get started.
There is something strangely satisfying about being able to take things apart and putting it back together. Inspired by the popularity of Lego sets in our childhoods, Minecraft brought this sense of wonder to video games.
For those of us who are life-long tinkerers who happen to be radiologists, I published in Radiology Data Quest a DIY on how one take DICOM apart and manipulate it. All in Python, no less.
DICOM is a pain in the neck. It also happens to be very helpful. As clinical radiologists, we expect post-processing, even taking them for granted. However, the magic that occurs behind the scene…
The Radiology Society of North America (RSNA) Annual Meeting is a place to expand your knowledge base, both by taking a deeper dive into your core interest and by getting your feet wet a few new skills.
If informatics is something you’ve been interested in but need a good way to get started, then the RSNA offers some solid opportunities for beginners. Continue reading →
After the recent presidential election, you are probably either particularly alarmed or especially excited about the outcome. Regardless of your particular political predilection, it is fair to say that this election puts data science on its head when so many got so wrong.
On an earlier issue of Harvard Business Review, the venerable magazine shared a piece of research from the University of Southern California on forecasting. When forecasting sales, the best estimators use a combination of intuition and logic – with both the logic-heavy and intuition-heavy forecasters performing less accurately.
In the age of artificial intelligence and big data, it can be sobering to realize that despite the staggering volume of data we are now collecting, ignoring your gut instincts can take a heavy toll on your decision-making abilities.
Source: “What type of forecaster are you?” Harvard Business Review (March): 26.
Big Data has become a radiology buzzword (the others: machine learning, AI, and disruptive innovation are also up there).
However, there is a real problem with using the term Big Data – it isn’t just one set of data problems. Big Data is a conglomerate of different data challenges: volume of data, heterogeneity of data, or the velocity of data are all important dimensions. Machine learning and internet of things are others layers superimposed on the big data problem.
Sometimes it is helpful to step back and approach data problems with a common framework, a way to think about how and which facets of data science fit in a real-life workflow in the face of an actual problem.
Below is a 6-element framework that helps me think about data-driven informatics problems. They are generally in chronological order, but they are not “steps” because you frequently will find yourself going back and redefining many things. However, the framework helps you maintain a big-picture outlook. The reason any sufficiently complex data problem requires a team approach. Continue reading →