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Machine Learning shows promise in Dementia prediction

Rubik's Cube

I was flipping through some archives, and found this Scope (a Stanford University, School of Medicine Publication) article that delineates a machine learning tool (link below, Scope calls it AI, the authors of the tool, in their paper, also linked below, rightfully categorize it as ML, a subset of AI as we generally describe it). I always love when you have access to the paper linked to a study. It always makes things easy.

So, the folks at McGill, trained an ML system using PET scans from people who demonstrate symptoms of mild cognitive impairment, to see who among them would develop Alzheimer’s, given that not all of them do. They taught the system to focus on the elevated protein expression in specific brain regions to train and make predictions.

Used on an independent set, the tool had an 84% prediction accuracy of dementia progression. Read more in the paper. I want to share a few thoughts below.

  1. I think tools like this will become the norm over time. However, right now, they lack the kind of standardization and maturity required for integration into clinical practice. I don’t mean to state that in a negative sense. Such efforts take time, effort and funding, of course.
  2. An 84% percent efficiency is not enough, not even for a supporting tool, not even when humans are completely in charge. This is also achieved through training with large data sets, the use of better algorithms and other improvement methodologies. This could also use some standardization, that can then be spread to all ML, DL and AI tools, which use imaging for diagnostics in healthcare.
  3. The future should consist of such tools passively (and when necessary, actively) siphoning your imaging and other data off your EHRs, and then parsing them to see if predictions can be made. This however, requires more groups such as the ADNI (Alzheimer’s Disease Neuroimaging Initiative), from whose participants the imaging and other data was used, collaborations from hospitals, insurance companies and governments.
  4. To improve diagnosis across ages, sexes, races and other discriminating factors, global co-operation would be required.
  5. Of course, we need to take various types of data, ranging from imaging to genetics, to epigenetics and other sources to make diagnosis quite efficient. Perhaps, this combination is one way to get around the 84% efficiency in this tool, till a time comes when imaging alone produces better results. At that point, say you make predictions based on imaging, genetics, lifestyle and other factors, and they all chime in. You can probably use whatever interventions are available (this is a key factor, missing in all the hype about machine learning. You learn something, yes, but what do you DO?) to delay, treat and cure patients.

When I find more such interesting studies, I will share similar and other thoughts on Machine Learning, Deep Learning and AI, and their impact on Healthcare.

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  1. The Stanford SCOPE Article:
  2. The McGill Paper:
  3. Image courtesy, Pexels:

Interesting AI+Health Imaging Investment and a few thoughts on where the field might be headed


This morning, a LinkedIn contact of mine shared an interesting MassDevice article about “Bay Labs”, a company focused on bringing Deep Learning to Cardiovascular Health Imaging (though they define themselves in broader terms as an algorithm company). It appears that Khosla Ventures (after the same much-hated Khosla from California), an existing investor (which must mean seed funding) has backed the organization with $6mn in series A funding.

What is interesting

  1. So far, what we have seen is, the really big companies, such as nVidia and G.E. working on backing up imaging with AI. They even recently announced a collaboration. This would have been a scary scenario for start-ups trying to break into the space. Even now, one should not take too much comfort. Bay Labs apparently won in an AI company contest conducted by nVidia. It clearly shows that big players are keeping an eye on the start up space, potentially looking to scoop up promising organizations.
  2. Though Bay Labs likes to talk about themselves in somewhat general terms; they state this on their website, “In order to serve the largest number of people, we aim to amplify the benefits of deep learning by providing high-performance algorithmic capabilities to assist with healthcare challenges on a global scale.”, they are at least currently focused on Deep Learning, as it relates to Imaging for Cardiovascular Health. Don’t get me wrong, I like both ends of the spectrum displayed here. They appear to have a broad vision of where they want to end up, and yet, as is important for a startup, they have a somewhat keen sense of focus.
    1. Given that, even with all the healthcare and lifestyle influences urged on by healthcare systems, governments, non-profits and others, cardiovascular diseases remain the cause behind the most deaths in the US and around the world. Thus, even within just this field, in imaging alone, the opportunities abound.
    2. Plus, cardiovascular health also happens to be one of the most expensive, and is a low hanging-fruit for startups, expecting to break into a new field and work towards financial success. The other field is probably oncology, which however is crowded with Watson and others. Still, I expect at least a handful of startups focused on various subsets of oncological diagnosis and treatment to emerge out of the woods as time progresses.
    3. It is also one of the fields where practitioners are prone to adopt cutting edge solutions.
  3. Bubbles: Bay Labs appears to be an organization founded on some sound principles. The next few should probably follow along the same path. One has to wonder though, at what point, in the not too far future  will we see organizations touting AI-this and AI-that, without any sound basis? This should be interesting to track.
  4. Algorithm Patents: The debate on whether software and algorithms can be patented, and under what conditions was never properly settled (what, ever is?). A new wave of companies touting algorithms will now come along, and generally speaking, take each other to court, giving headaches to Luddite Justices, such as in the US Supreme Court who are still hemming and hawing about allowing basic AV equipment within the courts (ironically AI has been trained to predict how individual US Supreme Court Judges would rule, with fairly good efficiency). This whole space is something to watch closely.
  5. Assets: Besides patents, there are other things to be concerned about here. Who ultimately owns the data? The results from what the Machine Learning and Deep Learning algorithms find? Are the findings marketable? If not, who pays to get this done? How do governments, healthcare systems and private organizations, all of which form the backbone of medicine share the costs and the benefits?
  6. Mergers & Acquisitions: Once the hype cycle is ridden and who owns what is also a bit more clear, comes the M&A song and dance. This also should be interesting to watch.
  7. Interoperability: Data interoperability in heatlhcare, is already a major headache. This is currently one of my core research areas. I am deeply interested in unraveling the challenges of how data should be cleaned and labeled to be used in AI applications. On the surface, it appears that all you have to do is collect the data, just transform it for various purposes – to upload to EHRs, to use in Machine Learning and so on and then get on with it. The underlying issues are much more complicated. Who collects the data? How is it blinded? Who pays for all this? How does the learning get applied? How do you glean precision medicine uses out of all this? And, many, many more…
  8. Regulatory Dilemmas: I have a series of blog posts planned on this topic, but let’s take a quick dive here. How do you get approvals for such AI systems? How do you fix problems? Should improvements, fed by data volume and algorithm changes require regulatory updates? What happens when an algorithm produces defective results? These and many others problems will need some type of resolution, and will create new problems and opportunities of their own.

Summary: In summary, I’d say, news of companies such as Bay Labs is good. However, headwinds lie ahead for them, and everyone in industry. We are looking at huge paradigm shifts and thus, not much is predictable. New issues will unfold while ones that seem dire right now, might become trivial. We are in for the ride of our lifetimes!


  1. The MassDevice Article:
  2. Bay Labs:
  3. Image, Courtesy Pexels: