9 ms·

AI is being developed to make MRI scans 10x faster. https://www.forbes.com/sites/samshead/2018/08/20/facebook-aims-to-make-mri-scans-10x-faster-with-nyu/#5a5f7

AI is being developed to make MRI scans 10x faster. https://www.forbes.com/sites/samshead/2018/08/20/facebook-aims-to-make-mri-scans-10x-faster-with-nyu/#5a5f7fd87a04 https://www.forbes.com/sites/samshead/2018/08/20/facebook-ai... If DeepMind is trained on millions of MRI’s, we might have better preventative medicine.

eismcc 8y ago
This assumes that’s what medical companies want. If we get closer to full value based care, then yes.
- xiphias2 8y ago
  Which medical company doesn't want to move costs down? You don't need high costs to charge a lot.
  - eismcc 8y ago
    If your business is making money on treating issues, preventing them isn’t aligned. Worst case, you neglect issues until they get to a point where you start making money. Legacy dialysis business is a good exsmple - they need ass-in-seats to justify clinics, so prevention isn’t as big a priority. Also, doctors often have shares in clinic revenue.
    - sergers 8y ago
      A friend of mind works for a software company that was utilizing Medq AI's brain bleed/hemorrhage detection Algorithm to identify urgent cases to be reviewed by a radiologist. They were pitching it to a medical group, and they were like that's great for patient care but how does it cut costs to the organization...
    - xiphias2 8y ago
      This makes sense, but Facebook / Google have enough resources to push their solutions onto the hospitals (give few years for free, maybe give the CEO some other fun stuff).
- skwb 8y ago
  MRI manufacturers want to develop products that bring on new functionality and ability to justify their prices. A brand new 1.5T scanner easily costs several MIL to make, and for a clinic to purchase one will have to fulfill a business case to justify the purchase. For the clinics, acquiring new service lines (such as adding cardiac) is one such way to do so even in a more value based care setting.
deepnotderp 8y ago
Idk. A lot of the applications of AI to compressed sensing are resulting in plausible but inaccurate data being filled in. This is fine for, say, video games, but not as great for medical imaging I'd guess.
- skwb 8y ago
  I find it somewhat interesting that purely analytical techniques can be replicated using CNNs. Sorta goes to show that there's a sorta divergence in a purely EECS method vs some of the newer approaches that are able to integrate advanced signal processing with deep learning. Hopefully these hybrid approaches are able to advance the field. That said, MOST of the current publications out there on this topic don't seem to be pushing the field that far compared to conventional signal processing approaches.
shereadsthenews 8y ago
Is that really the way to make the MRI faster? It seems like a time and motion problem. You lie in the machine for a long time but there's a lot of slack; the actual image process takes up barely any of the time. Remainder of time is spent waiting for things to whiz around and for the tech to click "next" on their console.
- krapht 8y ago
  That's the problem, current MRI's are like taking high-def pictures in raw format 1 pixel at a time. There's loads of redundant information in the raw image encoding. If there was some way to only image the information we needed, the process could go faster. Adding more "cameras" to our MRI so we can do parallel imaging is expensive.
- skwb 8y ago
  Depends on what you're trying to image. Tbh, if it takes 15 mins vs 30 mins to get an image of your knee, it's maybe not really that much of a game changer. From the perspective of a MRI purchaser (say clinic or hospital director), maybe you can squeeze 1 or 2 more cases in a day, but it seems like a marginal improvement overall. I think cardiac is clearly the exception where you bring on board potentially new functionality with quicker acquisition.
krapht 8y ago
"Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images, says Facebook. The key will be to train artificial neural networks to recognise the underlying structure of the images in order to fill in detail omitted from an accelerated scan." Ah, right, what I want is for an ANN to invent information in a medical image. It's one thing to upscale textures in a game, but I can't see the use for this at all in a medical imaging device.
- pnloyd 8y ago
  I'd assume (hope) they mean to infer data and not invent it.
- FeepingCreature 8y ago
  I don't care what it has to do, it can be drawing pentagrams in blood for all I care, as long as it has even a fraction better health outcomes to unassisted doctors I'm all for it. The point here is making people healthy, not providing some sort of validated theoretically clean design. If the rcts say it works, bring on the deep learning voodoo. (edit: I, for the record, want to trust in average accuracy.)
  - gdy 8y ago
    This is not how trust works
    - visarga 8y ago
      You mean it's not enough to trust in average accuracy, you want trust in specific instances of application?
      - nitrogen 8y ago
        Averages are not adequate descriptions of situations with bizarre outliers. An ML model that creates nonexistent detail in a medical image screams bizarre outliers.
ericcholis 8y ago
I think this was already achieved in Switzerland without AI: https://www.dagensmedicin.se/artiklar/2018/11/20/en-mix-av-bilder-ger-snabbare-mr/ https://www.dagensmedicin.se/artiklar/2018/11/20/en-mix-av-b...
- vonnik 8y ago
  That's Sweden
dontreact 8y ago
I have talked at length with several people in the FastMRI project and in my opinion it is actually very dangerous. There is no feasible way to validate that such a model will not hallucinate normal tissue in the presence of a rare abnormality. The argument often used is that advanced reconstruction techniques such as compressed sensing have not required validating against very rare abnormalities, however when deep neural networks get involved you have a nearly universal function approximator whose behavior is nowhere near as bounded. The FDA will probably set the low bar of validating that the reconstruction algorithm fares well in the face of a few abnormalities and call it a day, instead of the proper (admittedly infeasible) validation of testing against all abnormalities that the reconstruction algorithm will encounter. Facebook will probably happily jump over this low bar, and patients will get hurt.
- dzhiurgis 8y ago
  From what I understand there are tons of MRI applications. If you are looking for something exotic of course you will try to get a human, probably best in the field. But if you are trying to bring this to masses one can start with with something "simple" like bones or some some relatively trivial organ. Not that MRI prices themselves are dropping much - gotta wait for room temperature superconductors.
- skwb 8y ago
  To be honest I am highly skeptical that FB would be able to have the sort of institutional capacity to launch any sort of medical device health product. It takes a non trivial amount of effort and time to fully productize it (in terms of system integration/ product testing/ FDA submission). Unless there is involvement with one of the major MRI manufacturers, I don't foresee this going far. Many more players in the game that actually have a decent shot of getting something like this to market.
  - duado 8y ago
    The thing is that because the highest-ROI application of machine learning is online advertising, unquestionably the two deepest pools of machine learning talent are at Google and Facebook. So this may be able to overcome the obvious culture mismatch between "move fast and break things" and the FDA.
skwb 8y ago
I'll believe it if I see it at RSNA 2019.

eismcc 8y ago

This assumes that’s what medical companies want. If we get closer to full value based care, then yes.

xiphias2 8y ago
Which medical company doesn't want to move costs down? You don't need high costs to charge a lot.
- eismcc 8y ago
  If your business is making money on treating issues, preventing them isn’t aligned. Worst case, you neglect issues until they get to a point where you start making money. Legacy dialysis business is a good exsmple - they need ass-in-seats to justify clinics, so prevention isn’t as big a priority. Also, doctors often have shares in clinic revenue.
  - sergers 8y ago
    A friend of mind works for a software company that was utilizing Medq AI's brain bleed/hemorrhage detection Algorithm to identify urgent cases to be reviewed by a radiologist. They were pitching it to a medical group, and they were like that's great for patient care but how does it cut costs to the organization...
  - xiphias2 8y ago
    This makes sense, but Facebook / Google have enough resources to push their solutions onto the hospitals (give few years for free, maybe give the CEO some other fun stuff).
skwb 8y ago
MRI manufacturers want to develop products that bring on new functionality and ability to justify their prices. A brand new 1.5T scanner easily costs several MIL to make, and for a clinic to purchase one will have to fulfill a business case to justify the purchase. For the clinics, acquiring new service lines (such as adding cardiac) is one such way to do so even in a more value based care setting.

deepnotderp 8y ago

Idk. A lot of the applications of AI to compressed sensing are resulting in plausible but inaccurate data being filled in. This is fine for, say, video games, but not as great for medical imaging I'd guess.

skwb 8y ago
I find it somewhat interesting that purely analytical techniques can be replicated using CNNs. Sorta goes to show that there's a sorta divergence in a purely EECS method vs some of the newer approaches that are able to integrate advanced signal processing with deep learning. Hopefully these hybrid approaches are able to advance the field. That said, MOST of the current publications out there on this topic don't seem to be pushing the field that far compared to conventional signal processing approaches.

shereadsthenews 8y ago

Is that really the way to make the MRI faster? It seems like a time and motion problem. You lie in the machine for a long time but there's a lot of slack; the actual image process takes up barely any of the time. Remainder of time is spent waiting for things to whiz around and for the tech to click "next" on their console.

krapht 8y ago
That's the problem, current MRI's are like taking high-def pictures in raw format 1 pixel at a time. There's loads of redundant information in the raw image encoding. If there was some way to only image the information we needed, the process could go faster. Adding more "cameras" to our MRI so we can do parallel imaging is expensive.
skwb 8y ago
Depends on what you're trying to image. Tbh, if it takes 15 mins vs 30 mins to get an image of your knee, it's maybe not really that much of a game changer. From the perspective of a MRI purchaser (say clinic or hospital director), maybe you can squeeze 1 or 2 more cases in a day, but it seems like a marginal improvement overall. I think cardiac is clearly the exception where you bring on board potentially new functionality with quicker acquisition.

krapht 8y ago

"Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images, says Facebook. The key will be to train artificial neural networks to recognise the underlying structure of the images in order to fill in detail omitted from an accelerated scan." Ah, right, what I want is for an ANN to invent information in a medical image. It's one thing to upscale textures in a game, but I can't see the use for this at all in a medical imaging device.

pnloyd 8y ago
I'd assume (hope) they mean to infer data and not invent it.
FeepingCreature 8y ago
I don't care what it has to do, it can be drawing pentagrams in blood for all I care, as long as it has even a fraction better health outcomes to unassisted doctors I'm all for it. The point here is making people healthy, not providing some sort of validated theoretically clean design. If the rcts say it works, bring on the deep learning voodoo. (edit: I, for the record, want to trust in average accuracy.)
- gdy 8y ago
  This is not how trust works
  - visarga 8y ago
    You mean it's not enough to trust in average accuracy, you want trust in specific instances of application?
    - nitrogen 8y ago
      Averages are not adequate descriptions of situations with bizarre outliers. An ML model that creates nonexistent detail in a medical image screams bizarre outliers.

ericcholis 8y ago

I think this was already achieved in Switzerland without AI: https://www.dagensmedicin.se/artiklar/2018/11/20/en-mix-av-bilder-ger-snabbare-mr/ https://www.dagensmedicin.se/artiklar/2018/11/20/en-mix-av-b...

vonnik 8y ago
That's Sweden

dontreact 8y ago

I have talked at length with several people in the FastMRI project and in my opinion it is actually very dangerous. There is no feasible way to validate that such a model will not hallucinate normal tissue in the presence of a rare abnormality. The argument often used is that advanced reconstruction techniques such as compressed sensing have not required validating against very rare abnormalities, however when deep neural networks get involved you have a nearly universal function approximator whose behavior is nowhere near as bounded. The FDA will probably set the low bar of validating that the reconstruction algorithm fares well in the face of a few abnormalities and call it a day, instead of the proper (admittedly infeasible) validation of testing against all abnormalities that the reconstruction algorithm will encounter. Facebook will probably happily jump over this low bar, and patients will get hurt.

dzhiurgis 8y ago
From what I understand there are tons of MRI applications. If you are looking for something exotic of course you will try to get a human, probably best in the field. But if you are trying to bring this to masses one can start with with something "simple" like bones or some some relatively trivial organ. Not that MRI prices themselves are dropping much - gotta wait for room temperature superconductors.
skwb 8y ago
To be honest I am highly skeptical that FB would be able to have the sort of institutional capacity to launch any sort of medical device health product. It takes a non trivial amount of effort and time to fully productize it (in terms of system integration/ product testing/ FDA submission). Unless there is involvement with one of the major MRI manufacturers, I don't foresee this going far. Many more players in the game that actually have a decent shot of getting something like this to market.
- duado 8y ago
  The thing is that because the highest-ROI application of machine learning is online advertising, unquestionably the two deepest pools of machine learning talent are at Google and Facebook. So this may be able to overcome the obvious culture mismatch between "move fast and break things" and the FDA.

skwb 8y ago

I'll believe it if I see it at RSNA 2019.