In 2017, the Kaggle Data Science Bowl took aim at using machine learning and artificial intelligence to fight the leading cause of cancer death in the US among both men and women. Entrants were challenged to use a dataset of thousands of high-resolution pulmonary CT images to create new lung cancer detection algorithms. These algorithms were made to improve diagnosis and reduce false positive rates.
Of the 394 competing teams, which team received the top prize? A team combining members from both the Medicine and Computer Science Departments of Tsinghua University in China.
Competitions such as this are a great way to combine international talent with global problems. This style of teamwork is just scratching the surface of the infinite potential for advancement within our field through interactions between medical professionals and computer science.
During radiology training, we learn that a 3-cm, spiculated, soft-tissue attenuating lung mass has a very high probability of being cancer. Likewise, a 5-mm, smooth, calcified nodule has a very low probability of being cancer.
However, we also know that many pulmonary nodules lay somewhere in between our ability to accurately predict malignancy. The Fleischner Society worked very hard to offer a solution with its updated follow-up criteria in 2017, which included both size and density changes. However, we still can’t look at an 8-mm nodule with a slightly irregular border and say how likely it will be cancer.
To take the Kaggle Competition one step further, there is a very real possibility that Fleischner criteria (or its replacement) will be very customizable and lung nodule tracking will improve. We will dive more into this in Article 2.
These Kaggle teams use similar technology behind Facebook’s facial recognition. Have you ever wondered how they can determine who is in your photos? This technology is called deep learning, a subset of machine learning and a subset of artificial intelligence, and we will also dive deeper into these topics in Articles 3, 5 and 6.
These sorts of efforts are a testament to the open-source community, and how people are determined to find novel solutions to important problems by working together and sharing data.
Let’s take the contrarian view, that machine learning and artificial intelligence may portent to the obsolescence of the radiology specialty. Professor Geoffrey Hinton comes to mind.
Geoffrey Hinton is a very smart guy, but his lack of medical training on the nuances of the radiology specialty, such as image-guided biopsies, tumor board and discussion with our surgical colleagues, etc., has perhaps given him only a superficial view of our profession. Radiologists’ jobs will morph with new tools but will be around as long as we continue to assist our clinical colleagues.
The outcome of the Kaggle competition was also benefited by timing and available resources. Thanks to the huge video gaming market, for the first time we have cost effective high power computing called Graphical Processing Units (GPUs). More in Article 3. Another area we may take for granted is voice recognition. While we may or may not see day to day improvement, it has certainly improved over the past decade. More in Article 4.
Another very important piece to the puzzle is data. Lots of data. Lots of data that is properly labeled. The American College of Radiology (ACR) and Stanford are currently working on this. More in Article 5. (or 7).
Collaborative teams of computer scientists and medical professionals have amazing potential for development of field-changing algorithms. But when we talk about inserting these technologies into our daily workflow, or in the context of privacy, or data management — cue the crickets. More in article 8.
Before we go any further, we would like to formally introduce ourselves.
“This is the first of a series of 10 articles aimed to guide my colleagues. I have been tracking the growth of ML and medical applications since 2012 and following great mentors like Drs. Dreyer and Michalski. Informatics, particularly medical image utilization, has been a large part of my background. I received my radiology training in the US Navy after serving as a flight surgeon with the Marine Corps. My final Navy tour was in Okinawa, including a Radiology Department Head tour, before completing my Navy commitment and moving back to San Diego as an Angel Investor, entrepreneur, and informatics advisor and consultant. As of this writing, I have no relevant financial disclosures regarding this series.”
“I have a background in chemical engineering, and worked as an engineer for two years. During the job, I realized that I needed to make a larger impact on society, and I also wanted to learn to code. Thus, I applied to school, got in, and quit my job. I am currently a Biomedical Informatics Master’s student at the University of Texas Health Science Center in Houston and an Albert-Schweitzer Fellow. I am always learning, and I am excited to help others learn what I know. I hope that through this series of articles, people from the medical field to the machine learning field to just the average person can use this information to understand the current landscape of radiology and its relationship with technology advancements in artificial intelligence.”
We believe that through our disparate, but complementary skill sets, we can educate others about this exciting field.
Now that you know a little bit about us, you might be wondering whyshould you spend your time on our series.
During the series, we will start slow and review key terminology. We will also discuss enough recent historical progress to provide context and address new trends. If you are a little more advanced, please offer clarifying thoughts from personal experience in the comments. And of course, if you notice any erroneous text, we will humbly review those comments as well.
This series is not meant to be exhaustive, but these articles are meant to level the playing field when it comes to radiology and artificial intelligence. This field is rapidly changing, and there are a lot of moving parts. We are doing our part to educate and learn through this process.
Join us for an interesting set of articles curated by a seasoned radiologist who is technology focused and a student interested in understanding how ML and AI will affect the next generation of healthcare.
This article originally appeared in Towards Data Science