4 ms·
Research labs have been trying this for years with every spectroscopy known to man - the signal is just too weak. I don't see any reason to think Apple has figu
by highd 9y ago
Research labs have been trying this for years with every spectroscopy known to man - the signal is just too weak. I don't see any reason to think Apple has figured something out that labs of optics experts can't do on the benchtop.
- harigov 9y agoI think machine learning coupled with increase in sensitivity of the signals could make a difference.
- sillysaurus3 9y agoWhy would machine learning be able to overcome a lack of statistical power?
- cperciva 9y agoIt's not clear whether there's actually a lack of statistical power, or simply a lack of sufficient modelling of the noise.
- TeMPOraL 9y agoIt wouldn't, but maybe there is enough statistical power, but it's just tricky to process?
- harigov 9y agoDid you miss the part about sensitive sensors? A single sensor may not be enough to classify accurately but multiple sensors measuring different things can sometimes work. Such sensor fusion has worked incredibly well for IMU's and also in self-driving cars.
- lj3 9y agoThat's how the Glucotrack claims to work. From their site: GlucoTrack uses ultrasonic, electromagnetic and thermal technologies to non-invasively measure glucose levels in the blood. GlucoTrack is intended for use by Type 2 and pre-diabetics. GlucoTrack combines the following technologies: Ultrasound (speed of sound change within the tissue); Electromagnetic (conductivity of the tissue); Thermal (heat capacity of the tissue). It's not approved by the FDA, but it appears to be selling in Australia, New Zealand, Spain, China, Korea, Italy and some others.
- weego 9y agoYeah let's get some AI in there to help out, maybe building the front end in react native will solve some problems no one though of. We could get the data with graphql to solve some issues.
- deleted 9y ago[deleted]
- harigov 9y agoExcept, it actually does. ML is just advanced statistical modeling which performs complex computation over large amounts of data to give out better results. Because Apple can afford to do things at scale and use their marketing prowess to buy sensors at an economical price, they can afford to use multiple sensors to give out a reasonably accurate metric. All the technologies that you mentioned do solve problems they are intended to solve. Why so much negativity?
- rubidium 9y agohahaha. Siri can't even parse "What time does the Indianapolis 500 start?" It gives me movie start times.
- harigov 9y agoThey are totally different things. Sure, Siri can't parse that and yet we have working* self-driving cars and autopilots for planes.
- manmal 9y agoJust consider Apple as yet another lab of optics experts, but with 100x the funding, 10y of material science advancements, and years instead of months of dedicated effort. It's not that hard to believe that this team could finally succeed.
- LanceH 9y agoMaybe Apple will tell their users with t2 diabetes they are eating wrong, roll out a new streamlined diet, and everyone gets better.
- pawadu 9y agoBut how is that different from the Google contact lenses from 2014?
- analog31 9y agoIndeed, I design similar equipment for industrial use. Non invasive blood glucose has been a holy grail of analytical technology for decades. Lots of smart people have gone bankrupt trying to solve this problem. The sensors are probably good enough already. Algorithms for sorting out massive libraries of spectral data exist. It's a hard problem. That's not saying Apple doesn't stand a chance, but that they're entering a very mature space.
- Kluny 9y agoThat is supposed to be Apple's strength though, isn't it? They never do anything first, they just do it right first.
- analog31 9y agoIndeed, but those successes have tended to be in design and marketing -- not to denigrate those fields, but this is basic science, and hasn't even been done first yet.
- kgin 9y agohttp://www.gluco-wise.com http://www.gluco-wise.com