Tag Archives

2 Articles
<span class="live-editor-title live-editor-title-145" data-post-id="145" data-post-date="2019-04-07 06:40:50">Roche study solidifies AI’s immense value in diagnostics</span>

Roche study solidifies AI’s immense value in diagnostics

 

I have said this before, whenever speaking about AI in the context of healthcare, and here we are, with Roche, once again proving that AI has a huge potential to transform diagnostics. Now, I also strongly believe that AI (and in the current context we are talking about Narrow, Weak AI) has the potential to transform therapy and the fused emerging field, theragnostics. However, diagnostics is a low-hanging fruit, and will be first conquered by AI.

The current Roche study

Roche scientists deserve kudos for fighting genericide, having picked Diabetes Macular Edema (DME), a specific condition affecting specific cohorts within those living with Diabetes. The biggest effect of diabetes is in the secondary effects that cause more harm to patients many a time. Without appropriate diagnosis, Macular Degeneration can lead to permanent damage and consequent degeneration of sight.

The choice of a specific eye condition is not a weakness, but a very good way to conduct research, and make a comparison between AI, which uses the two-dmensional Color Fundus Photos (CFP) versus the three-dimensional Optical Coherence Tomography (OCT), which is the gold standard, and requires human expertise for successful diagnosis. Plus, as Susan Shepard lays out in her review (link below), the former technique is available through telemedicine centers, while the latter is not, as it is more expensive as well. This also has implications for underserved populations in the developing world, as diabetes is a global health condition.

AI and Imaging based Diagnostics

Another aspect that makes this particular study powerful, and also demonstrates the power of narrow AI, specifically Deep Learning is the choice of diagnostics that relies on imaging. Over the past few decades, quite a bit of research and development has focused on making Deep Learning powerful, especially when it comes to imaging viz-a-viz applications in facial recognition and more broadly, image recognition. This has resulted in knowledge in the form of algorithms and techniques that can now be successfully extended to AI.

 

 

Explainability

The authors of the study also allude to the fact that they have been able to get the algorithm to spit out its secrets as to how it is arriving at the diagnoses. This is a fundamental requirement of AI. The more an algorithm is understood in terms of how it recognizes patterns and arrives at conclusions, the more value it has in aiding diagnoses.

Explainability is critical, not just for healthcare applications, but in many fields where AI will be put to use. It has also been an ongoing challenge, and will be a significant hurdle when it comes to application. It is of such high importance that DARPA is continuing to make investments to have explainability or XAI be a functional component of AI going forward.

 

Where we go from here

AI can make diagnosis, like  many other activities and tasks faster, cheaper and more accurate, supplementing and complementing humans. As I mentioned before, this can have an effect and applications of immense value with very high Returns on Investment (ROI) for governments, private organizations, patients and stakeholders sundry. There are also several social applications where it will greatly benefit humanity.

There will be progressive churn, and the competition to make AI a market reality, already very intense is only going to grow stronger over time.

Yet, I do not expect the transition to be smooth, and there will definitely bumps in the road ahead, in terms of technological, regulatory and other challenges.

 

References:

  1. The Qmed Article by Susan Shepard: https://www.mddionline.com/study-shows-using-ai-eye-screenings-could-improve-vision-outcomes?ADTRK=UBM&elq_mid=8095&elq_cid=74447
  2. Close-up Image of the Eye: https://www.pexels.com/photo/eye-iris-anatomy-biology-8588/
  3. Image of person checking blood sugar: https://pixabay.com/photos/diabetes-blood-finger-glucose-777002/
  4. Image of X-ray: https://unsplash.com/photos/ouyjDk-KdfY
  5. Image of Doctors Collaborating: https://stocksnap.io/photo/GSBJXWRSDV
  6. Image of Subway Stairs: https://burst.shopify.com/photos/up-subway-stairs?q=stairs

Interesting AI & Immune System Initiative by Microsoft and Adaptive Biotechnologies that leaves behind some questions

It is no secret that the larger technology companies including Google, Microsoft, nVidia, IBM, Apple and others want to dominate AI, as well as healthcare, in an ever expanding competitive landscape. While it is anyone’s guess if they will succeed, or get upstaged by smaller, nimbler firms in either arena, the moves they make are definitely interesting to watch. A lot of the moves appear benign, but could lead to cannibalization, such as the “AI Contests” some of the organizations put up (more posts to come on this).

Partnerships can go both ways I suppose, and are probably a strategic way to externalize any risk of failure. In that sense, in the current example, both Microsoft and Adaptive Biotechnologies appear to want to play up their strengths. The premise of what they want to do with the partnership is also quite intriguing. You can read it from the horses’ mouths in the links provided below. I will summarize them and lay out a couple of thoughts that come to my mind.

Essentially, the project would turn the body’s immune system itself into the data source for diagnosis. Because every time the immune system responds to a disease, T-Cell receptor (TCR) proteins are expressed to combat antigens. Mapping the TCRs, through a simple blood test, as Microsoft and Adaptive postulate, can go a long way in early diagnoses of an array of diseases. To say the least, the project is ambitious, and here are a few thoughts:

  1. Accurate diagnosis and personalized therapy require knowledge of the state of the human body and its disease. Simply mapping the genetics of a person, and considering their epigenetics and their lifestyles, etc. is complex enough, but it still might not be enough! Thus, TCRs could be mapped and allow for quicker diagnoses, if the theory pans out on a large-scale. It is yet unclear to me that mapped TCRs can actually yield the necessary diagnostic clues, machine learning or not, for a larger variety of diseases. However, it might supplement diagnostic efforts alongside genetics, epigenetics and other health data sources.
  2. From a business angle, I also find this to be intriguingly different from the general bedlam of text processing a la Watson, and all the algorithms rushing to read and reinterpret imaging as with nVidia and others. Microsoft has appeared to have looked for and found a partner with a unique approach to the application of machine learning in healthcare.
  3. Any large set of unknown targets, powered by data might appear to be a classic problem for machine learning to solve. However, Microsoft and Adaptive (Microsoft has invested in them now, apparently) might have joined a Kool-Aid club that bridles the horses behind carts. What I mean when I array out those cliches is this: medicine already has a problem of knowledge paucity when it comes to diagnoses, until more clarity becomes available, by way of a progressed disease. This is fundamentally because disease precursors are poorly understood, from want of clinical research, not lack of intelligence. What is to say the TCRs won’t just set off an array of false and confounding alarms? Yes, with liquid biopsy and other such hyped up methods out there, the industry is in a rush for quick fixes. It might well turn out that this is much harder to resolve, with clinical studies and protocols that will require to demonstrate that TCR expression, their proportional presence, etc., do truly indicate the preliminary stages of a disease being present in the body. I am not convinced yet.
  4. There is also a maddening rush out the gate to define universal tests with pinpricks of blood. While I am not suggesting we practice good old, barbaric bloodletting (although there are all kinds of people out there “thirsting” for a comeback to this practice), I think this is an unnecessarily over-constrained problem definition, perhaps making titillating fodder for press releases and blog posts. There might also be an urge to combine these pinprick tests with diabetes monitoring and such. While it is tempting to fantasize about such possibilities, and at some point, these might come to fruition, there is no need to go to such extremes before solving fundamental problems in medicineaccurate diagnosis and targeted therapy. For example, when should a person’s blood be drawn? How frequently? Would the frequency vary when a certain set of TCRs are observed? There are so many things to worry about here. I would think companies would stop using overly broad terms such as “universal”.
  5. In my posts here, in my talks, daily discussions and so on, I always come back to a few bugaboos. Who will own the TCR mappings? Who owns the product of the machine learning algorithms? Will they be patented and bridled off? How will such diagnostic methods be regulated? Validated? Will Explainable AI, something I expect to be a fundamental principle that should be applied to healthcare be required (see explanation from DARPA linked below) and used judiciously? And on and on we go.
  6. Data has been walled off quite well in the healthcare industry up to this point. Yes, we got the human genome, but much much more sits behind curtains and masks and other cliches you can think off, that every new technology that promises to expose and dig through data always concerns me, surrounding ownership.
  7. The “Theranos” Effect: If you are like me, and know about the story of Theranos, you are still sitting up at nights, jaws dropped, wondering how in the hell, the company is still in vogue (I have written about this on my medical devices blog, in fact, using the same Pixabay image! See link below). I have also linked, one of several dozen well-written write ups that offer us a tale of caution, and I plan to call this, as I have named it, the “Theranos” Effect. In summary, this company went into the “over promise, and extreme under-delivery” (or never delivery, to date..) business. They engaged in egregious and unethical business practices, fooling the industry, investors, partners and more, along the way. How do we make sure that with all the promise of AI, companies don’t make such ugly incidents repeat themselves? Mind you, this is not me pointing fingers at Microsoft. I think this is indeed a great effort. I am just offering this up as an important tale of caution, for people in healthcare, and in any industry for that matter. I understand, as much as anyone else that businesses need hype to push their products. However, it would behoove you to make sure you don’t push things off a cliff…

CONCLUSION

In mankind’s march towards the goal of a healthy future for all, we have many strides to make. Naturally, we want to be as accurate and as thorough, yet economic as we can. Therefore, we rely on technological breakthroughs on one end, such as anything ranging from improvements in basic science, to sensors and AI, and on the economics of lower thresholds for test materials consumed, time to diagnosis and other aspects on the other end. What Microsoft and Adaptive aim to do with their (investment based) symbiotic looking partnership is commendable. It may take us one step closer to our goal, but it may not be the one to take us there at all. Only time can tell, and in the meanwhile, I hope commonsense and ethics prevail over hype and fantastic marketing materials.

Should you have something to add, please leave a comment below.

Subscribe and Support, Please! 

Did you enjoy this post? Please subscribe for more updates, using the sidebar. Have ideas or blog posts you’d like to see here? Contact me at yamanoor at gmail dot com.

References:

  1. The Microsoft Blog Post: https://blogs.microsoft.com/blog/2018/01/04/microsoft-adaptive-biotechnologies-announce-partnership-using-ai-decode-immune-system-diagnose-treat-disease/?imm_mid=0fa701&cmp=em-data-na-na-newsltr_ai_20180115
  2. Adaptive’s Press Release: https://www.businesswire.com/news/home/20180104005464/en/Adaptive-Biotechnologies-Announces-Partnership-Microsoft-Decode-Human
  3. DARPA, on Explainable AI: https://www.darpa.mil/program/explainable-artificial-intelligence
  4. Vanity Fair on Theranos: https://www.vanityfair.com/news/2016/09/elizabeth-holmes-theranos-exclusive
  5. Myself, writing with incredulity on Theranos’s longevity: http://chaaraka.blogspot.com/2017/12/theranos-lives-to-die-another-day.html
  6. Image, Courtesy, Pexels+Pixabay: https://www.pexels.com/photo/white-and-clear-glass-syringe-161628/