The Open Source Revolution

The open source movement is revolutionising medicine. Never before in human history has there been such knowledge and opportunity available to anyone with perseverance and a connected device. In fact with enough patience, there are multiple, perhaps seemingly infinite tools and skills one can acquire, that enable quite sophisticated analysis of medical images (among many other areas of science and medicine). I’d like to explain how the ‘stars have aligned’ for this revolution and glimpse future possibilities, whilst also acknowledging a degree of hype surrounding AI and its application to medicine. 

Punch card data entry. Wikimedia Commons here

To put the present in some sort of context, my father-in-law took a computer subject at university in the seventies. In large groups, one of their assignments was to punch holes into a long piece of paper which they fed into a computer to produce a very basic game of ping pong. This computer was state-of-the-art at the time and took up several stories of the university. Mobile phone users are expected to tick over 5 billion next year, each of these capable of providing vast amounts of knowledge and at least theoretical training for many different skills to anyone who can afford one (not everyone). Who knows what computational power and device size will be common-place in another 3 or 4 decades. Futurist Ray Kurzweil has, for example, predicted that by 2049, one computer will have more computational power than the entire human race combined.

Radiopaedia was preceded by more general open source platforms. All manner of these now inhabit many corners of the web, including growing and increasingly comprehensive biobanks rich with patient-level data. The gradual specialization of open source sites is not unique to science and medicine. For example, there are now numerous open source communities that foster the learning and progress of programming languages like python. Whilst vanilla linear and logistic regression have been around since the 1950s, now with a few lines of code we have devices that can crunch these algorithms en masse. Enter machine learning. For free at, you can spend a few hours (ok probably days or weeks) and process millions of data points to draw insights and make reasonable predictions about new data. If you are pressed for time or perhaps less technically inclined, there are palatable discussions of cutting-edge technologies: features regular podcasts from a data scientist explaining concepts to his non-data scientist partner. This one is a great start for anyone curious about applying machine learning to medical imaging. The scope and complexity of mathematical models for predictive and other analytics continues to expand and with open source code, you don’t have to be Good Will Hunting to enact them. Using radiomics techniques to predict mortality from chest CTs has been conceptually proven (Oakden-Rayner et al 2017). Machine learning (including deep learning) ought to expand the detection of pre-clinical disease states prior to the patient developing symptoms and could be a stimulus for much wider uptake of medical imaging. Such a proliferation of image acquisition poses another set of questions to the radiology field. Pre-clinical detection is applicable to some diseases more than others but perhaps even apparently unforeseeable conditions like major trauma will one day be accurately predicted by a network of biobanks, machine learning algorithms and an internet of things (IoT). Regardless, technological advances spur on precision medicine which will eventually be genome and probably environment specific. Social and ethical debates about how this may widen the gap between the ‘haves’ and ‘have-nots’ are inevitable and desirable. There are many examples of how technological advances disperse for global benefit; mobile phones and Radiopaedia itself are great examples of these.

At least for the next 30 years, there will always be radiologists in some form or another. We are a long way from any form of AI being able to listen to, digest and give salient advice about complex medical histories and examinations; point out pivotal features in selecting different modalities to colleagues; be perspicacious in high-stakes multi-disciplinary meetings or perform complicated procedures. There are also less easily defined roles for the human touch, the laying-on-of-hands or the thoughtful, attentive and knowing nod that patients appreciate and as any clinician will identify, can be seemingly therapeutic in and of themselves. For the foreseeable future, deep learning algorithms rely on more than just a handful of examples for a given condition. The deep learning results prompted by the UK’s NIH open source chest x-ray database, whilst pivotal and theoretically exciting, have been confined to certain entities (eg pneumonia, cardiomegaly, pneumothorax etc) and not currently feasible for workstation, coal-face translation. It will be a while yet before workstation software can effectively point out uncommon findings like Luftsichel sign. So for at least a few decades to come, Radiopaedia will be a valid tool for us humans recognizing rare and uncommon conditions and trainees will still be pouring over thousands of chest x-rays each.

This combination of open source capabilities is the very exciting infancy of radiomics - beyond what is (my new favorite term...) ‘human-readable’. We can now process medical image data on a scale that would make Wilhelm Roentgen physically (and metaphysically) ill. It is an incredibly exciting time to be a part of what some are calling ‘the fourth industrial revolution’. Only time will tell if these kind of statements are hype but for sure, we have only just now witnessed the tip of the open source, medical data iceberg and Radiopaedia is strapped in for the ride.

About the author: James Condon graduated from medicine 2014 and is commencing as a PhD candidate 2018 in the use of computer vision for medical image interpretation. He works casually in emergency medicine and clinical trials and has previously completed a range of medical and surgical rotations in Adelaide.

Disclosure: J. Condon is commencing independent post-graduate research with G. Carneiro and L. Palmer, co-authors of a journal article referenced in this piece. They were not involved in the writing of this blog. 

Disclaimer: Views expressed in blog posts are those of the author and not necessarily those of or his/her employer. 

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Publication date: 22nd Apr 2018 22:00 UTC

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