One thing that I'd point out is that this comes entirely from the press rather than the startup - we see ourselves in a totally different sub-industry from Theranos (Athelas is a single type of blood test, at-home device, specifically for oncology patients, and a totally different technology). Really the only common word is "blood", but unfortunately I think that often gets drowned out in the media
The $20/mo is for the device and the test strips per month, as such the device is rented. Each subscription comes with 10 test strips a month usable between different users!
CTCs are a very early application in our tech and one that we're excited to continue working on. We have some strong sample share relationships with Stanford and sensitivity/stage/specificity testing is in its early stages right now. Will publish on the data page when more information
-The first line of defense against interstitial fluid is wiping away the first drop (commonly used protocol in most drop test). The inevitable remaining interstitial fluid chunk is then classified out by our image processing models, which have been trained to recognize differences in images based on debris/cell concentration.
-We currently report the WBC, WBC Differential, Platelets, and RBC indices. The flags we're missing are secondary RBC metrics like MCH and RDW estimates but we have plans to roll these out soon as well.
-Yes, we've run both bench and clinical testing on patients with north of 100k white blood cells - the image processing can segment out cell chunks in these crowded samples better than traditional flow cytometry. Not in the currently published studies, but some good data coming out about this soon.
-We've run basic initial tests on platelet clumping (artificially induced in certain diluted samples), and present in clinical samples. Since our model looks at the boundaries of these platelet cells, we have a lot less trouble with this than flow cytometry/beckman and even human pathologists. Still, we're working to find more samples with clumping to define the limits of detection on this front.
-We've done interference studies with Hemolysis, EDTA, not with lipemia and icetrus. These are on our list of clinical samples to source as well.
-We've begun working with some of these EMR providers, the good thing is many of them have sandboxes to get setup in relatively quickly. The larger ones do have long, long-term engagement timelines.
-We work with dozens of pathologists to help review results, train the system further, and in general go over good morphology practice. Has definitely been a core part of our strategy.
Dhruv's planning on putting out a series of posts, where he'll probably be including the model training/set up components! Look out for the next bits this coming week.
Athelas is hiring full stack engineers (react/mobile, python, js, backend). We're making blood diagnostics low-cost and decentralized. Send resumes and projects to tanay[at]getathelas.com
Interesting - thanks for the feedback! have been internally chatting about making the video beginning/in general more technically focused as opposed to overly emotional (something we noticed was coming about), helpful to hear that here as well. I think the direction we were taking was based on clinical fact (lower cost, earlier testing esp for baseline metrics strongly associated with better clinical outcomes), but I can see how it comes across with that over-hype vibe.
There are a lot of people we've started working with (elderly populations, old age homes) for whom a monthly test is really useful (at-risk for a lot of WBC screenable conditions), but who don't want to buy an entire device/learn to use with app and related software. The monthly screen came as an offshoot of this population's use case.
While the front-facing website is a direct to consumer device, the majority of our deployed devices will be partnerships formed with clinics - this just enables anyone who wants to use the tech to be able to without the long email, custom quote, phone call, partnership process.
We have two versions of our device - one that's fully automated (the one requiring a 510k), and another where results are manually confirmed post computer vision in-house before being tabulated (this is classified as a "manual count" or hemacytometer). To start off distributing, we're shipping with the Class 1 version to beta users and early locations. Once the FDA clears our algorithms to be fully automated (under class 2), that won't be needed anymore. Let me know if that makes sense
Definitely! Not all of what Theranos did is public, so I can only share from what I know, but our approach is highly focused on trained computer vision algorithms - whereas a lot of previous approaches have relied on coulter counting (mentioned in the post). You can check out how we performed in the data section http://Athelas.com/data
Currently taking CS 103 (Mathematical Foundations of Computing) under Keith at Stanford. Truly one of the best taught classes I've ever taken - the man has a love for teaching Computer Science that really makes all the difference.
As someone working in this field particularly on the product front and academic front - a major concern I have with this study is the lack of work done to establish what the clinical significance is in these variations. The methodology is well controlled enough to indicate that a statistically significant difference does indeed exist in the variations on the drop-to-drop level between venous and capillary samples, but what's missing is a detailed analysis of whether or not these differences would result in clinically different outcomes - from my work, the range of identifying an anemic, leukocyte spikes, etc. is large enough that the spikes in deviations in capillary samples ultimately become inconsequential. Furthermore dozens of studies [1,2,3 are just a few examples] in the past have found essentially the opposite outcome. A discussion is necessary - but suggesting that all drop based diagnostics will forever be inaccurate is both unbased and dangerous given the growing importance of this field. If anyone has specific questions feel free to drop me a line at ttandon[at]stanford[dot]edu
My dad went to college with Amit in India - he and his friends always speak incredibly well of him but simultaneously express wonderment as to how it was he who made the rise, given that many others were more 'brilliant', class-toppers, etc. so to speak. I think it's amazingly interesting how true brilliance manifests itself in different ways than we could expect a conventional system to indicate.