6 ms·

"Radiologists, because they have a grounded brain model, only need to see a single example of a rare and obscure condition to both remember it and identify it i

"Radiologists, because they have a grounded brain model, only need to see a single example of a rare and obscure condition to both remember it and identify it in the future." This would actually be a long term reason to go for AI / database diagnosis. I had a personal case where a close family member, a young child, almost died. The doctors didn't understand condition and last minute it suddenly calmed down. Im sure a few doctor's in the world seen it before, but they weren't working in my hospital that week. If we can start sharing in-depth diagnosis worldwide of obscure cases using AI to make it easy to query that would probably be of great benefit.

srvmshr 3y ago
That's one of the biggest roadblocks: hospital & healthcare entities will not not have permissible mass sharing of data. When switching hospitals, its really hard to get a full copy of the record for single person. Try that for whole population. I may be wrong, but hospitals also could have incentives to tightly hold on to their proprietary data - where they spent a large sum of money. I worked in ML+Dermatology & getting data was a lot of regulatory check. Also, DICOM is the format for maximum interoperability from different radiology modalities & manufacturers. But annotation & markup methods vastly vary between institutions. There is no commonality or agreed upon standards on information interchange.
- samuell 3y ago
  Federated learning (with implementations such as FEDn [1]) is supposed to solve this problem; Only sharing training weights, but not sharing data. Sure requires some coordination, but the legal parts should at least be solvable in this way. [1] https://github.com/scaleoutsystems/fedn https://github.com/scaleoutsystems/fedn
  - haldujai 3y ago
    I've been involved in a federated learning radiology project, the biggest issue is image technique and labelling. Different centers practice very differently, with different imaging protocols, disease prevalences, and labelling/reporting. This project was looking at renal masses and the only part that worked well with federated learning was image segmentation and probability that the mass is a cancer, this is a competency I expect out of a first or second year trainee. It was horrible for predicting subtype of cancer as we couldn't get a good training set (few of these lesions are biopsied, specific MRI sequences that may help are not done the same way in every center) which is what the goal was and more of an experienced generalist/subspecialty radiologist skill. Practically subtype doesn't make too much of a difference for the patient as if it's "probably a cancer" it'll just get cut out anyway, but highlights a challenge with federated learning.
    - chaxor 3y ago
      BERT-like encoder models actually do help here, although unfortunately the dense representation is not as nice and clean as a class, it will at least map to something similar. If the hospital provides even a completely bizarre entity id, the requirement would be to provide the dictionary of descriptions and train the BERT-like model to associate the entity ids and descriptions.
      - haldujai 3y ago
        > BERT-like encoder models actually do help here, although unfortunately the dense representation is not as nice and clean as a class, it will at least map to something similar. Completely agree with this. > If the hospital provides even a completely bizarre entity id This is where it beings to fall apart, the hospital doesn't always provide an entity. In this specific project, some will argue "it's a big enough renal mass just cut it out, based on current evidence it's safest to remove" which while true at the moment doesn't help us in developing a model to prospectively predict the subtypes that do/do not need surgical excision. Consequently, there is no biopsy and specific MRI sequences that are proving helpful at subtyping are missing, irrelevant for clinical care per current standard practice but hugely relevant if you're trying to change that practice as we need labelled data but we don't have the resources to have a radiologist/pathologist go over every case again and try to fill in some blanks en masse.
  - wouldbecouldbe 3y ago
    Im not sure it even has to be complicated and full on machine learning. Could also just make it easy to share diagnosis to the a database stripped from personal information about the client, properly index it, share it worldwide, allow doctors to contact eachother automatically who match.
    - distant_hat 3y ago
      If it is a rare condition, reidentifying people from even small amounts of relevant data (age, gender, hospital visited) data is trivial. If it is a common condition, usually there is plenty of data already there.
  - renonce 3y ago
    Is that really possible? I don't understand the field exactly but at least if you use deep learning, you'll be able to recover inputs from gradients, and if you want to program it such that no other party is able to recover your inputs, that's called cryptography and homomorphic encryption is FAR from practical yet (like a million times slower than practical). Without rigid mathematical foundations, I would doubt that it's just a fancy way of gathering all the training data and doing the training and making it perceive as if everyone retained their own data.
- wouldbecouldbe 3y ago
  Yeah you have to make it non-personal, as well as deal with the commercial side of it. I've seen a few initiatives, but they all seem to lack the pockets, drive and ambition of an arrogant Silicon startup. Even though it's annoying at times, it might be what can do the trick in this case.
- sampo 3y ago
  > That's one of the biggest roadblocks: hospital & healthcare entities will not not have permissible mass sharing of data. Maybe in some other country?
  - srvmshr 3y ago
    I am not certain how it works in all of Europe, but it doesn't seem straightforward in Sweden or UK. In US and Japan, they will rather do the full roster of tests than transfer in radiology results from another hospital. Its not something to do with costs, but physician opinions. Every specialist has his personalized view of the patient in hand - so the corpus of information he needs is different.
  - brabel 3y ago
    As far as I know, in Sweden data about a patient is shared between every hospital... you need to sign up on a web portal, of course, but from then on, you can authorize a doctor or nurse anywhere to get your data with a click... you can go to any clinic and they have access to your full history. It's impossible to do anything properly if you don't have that. I just don't understand why in the US, it's acceptable for hospitals to be silo-ed like that to the detriment of everyone's health. Here's a news report from 2017, when the system was being set up: https://www.bmj.com/content/357/bmj.j2069 https://www.bmj.com/content/357/bmj.j2069 Today, this has been working for years.
    - hattmall 3y ago
      The US has multiple individual medical systems larger than Sweden with essentially the same abilities. Sweden has roughly the same number of hospitals as South Carolina.
      - brabel 3y ago
        What makes you think the Swedish system wouldn't scale to countries with 100's of millions of people??
        3y ago
- mtkhaos 3y ago
  What's nice is considering a global audience.
- dustingetz 3y ago
  this may be a sufficiently powerful macro force to disrupt that
- weatherlite 3y ago
  I think it will happen much faster in China, India or maybe Japan than in the U.S for that exact reason. It's quite possible the U.S will lag a lot in healthcare soon.
srge 3y ago
Exactly: what if my radiologist doesn’t know my particular rare condition? I would feel comfortable removing the human diagnostician. Let’s have actual human doctors acting as researchers working to improve the AI diagnostics.
- haldujai 3y ago
  My perspective as both a radiologist and CS/AI researcher so exactly what you're proposing: 1. We don't practice in imaginary vacuums, it's easy* to identify that something looks abnormal and then refer to clinical resources/other physicians for rare diseases with specific questions in mind (i.e. recognizing the nuanced imaging finding and referring to a resource to assist). As a rare disease example, tumors of the eyeball/orbit are very rare, but detecting them is not. If I open the case and see one I can refer to StatDx to help me narrow my differential knowing what imaging features I'm looking for. This reduces my misdiagnosis rate (which as an aside is ~2-5% for radiologists, "major" clinically significant that impact morbidity or mortality ~2-10% of those depending on the study). 2. Rare disease are hard to diagnose, and would likely also be hard for an AI. Imaging appearances are not unique to the vast majority of diseases, especially what we cal the "weird and wonderful". Pelvic tuberculosis, endometriosis, advanced cervical cancer and advanced rectal cancer can look identical/nearly identical on MRI and the clinical portion as well as additional testing helps us get to the diagnosis. We don't have to diagnose everything based on a single imaging test, nor should we given: 3. Diagnosis is a tradeoff of sensitivity and specificity. You can't have both. Let's consider adrenal gland tumors. Statistically these are going to be benign, there is no specific imaging feature to tell a small adrenocortical carcinoma (ACC) ~1 in 1 million incidence from an adrenal adenoma (99% of adrenal lesions). We also can't tell them apart with a biopsy under a microscope. If you're unlucky enough to get an ACC you're basically shit out of luck as the only options we have are to recommend adrenal surgery (and their complications which can be death) to optimize sensitivity, or assume that it's benign and optimize for specificity considering disease prevalence and risk of overdiagnosis. In practice, we just use a cutoff of 4cm. I'm not sure how an AI would solve this, especially as there isn't a large enough training set. MD Anderson has the most experience of any center and they've had ~600 cases in 40+ years which as you can imagine encompasses a very heterogeneous imaging set (we didn't have multidetector CT or 3T abdominal MRI 20+ years ago). Overall, AI can and should help radiologists and as someone involved in this field I can't envision a world where we can safely remove the human diagnostician element from the mix, given that it's a spectrum of grey not black/white labelling as it is for object detection. We've had attempts with mammography and stroke AI and it's still horrendously inaccurate compared to what I expect out of a resident radiologist let alone an experience staff physician.
gonzo41 3y ago
You need to pair a system like this with regulation and changes in the medical field to upskill nurses so they have a larger extended skill set and essentially have a path to becoming doctors via practical experience. There's a lot of information on medical conditions. I remember hearing about IPhone apps that can get a 90% hit rate on correct diagnosis for general practitioner style visits. Taking that into account, and a large scale medical database, you'd think we'd be pushing that technology down so we can get better productivity out of primary care, so most of the time Dr's were using their big brains for big complex problems.
- wouldbecouldbe 3y ago
  90% hit rate is pretty easy to accomplish for a standard doctor's visit, a big percentage of the doctor's visit are standard. You can probably train most humans to deal with that quickly. They often use students if the pressure is high here. It's about the other 10% + unforeseen issues.
  - haldujai 3y ago
    Speaking to radiology, ~90% of imaging studies are normal so I can sign my default template without looking at the images and be correct the vast majority of the time.
    - cicce19 3y ago
      Also a rad (and heavily in the AI field). I think you mean 90% have inconsequential findings and not that they are "normal". Can you really apply untouched templates to 90% of your cases? I find that very hard to believe. How many templates do you have and how can they possibly describe the variety of possible findings seen on each type of study?
  - taneq 3y ago
    90% hit rate could probably be achieved with "take some panadol and come back in a couple of days if you still feel bad."
- 3y ago

srvmshr 3y ago

That's one of the biggest roadblocks: hospital & healthcare entities will not not have permissible mass sharing of data. When switching hospitals, its really hard to get a full copy of the record for single person. Try that for whole population. I may be wrong, but hospitals also could have incentives to tightly hold on to their proprietary data - where they spent a large sum of money. I worked in ML+Dermatology & getting data was a lot of regulatory check. Also, DICOM is the format for maximum interoperability from different radiology modalities & manufacturers. But annotation & markup methods vastly vary between institutions. There is no commonality or agreed upon standards on information interchange.

samuell 3y ago
Federated learning (with implementations such as FEDn [1]) is supposed to solve this problem; Only sharing training weights, but not sharing data. Sure requires some coordination, but the legal parts should at least be solvable in this way. [1] https://github.com/scaleoutsystems/fedn https://github.com/scaleoutsystems/fedn
- haldujai 3y ago
  I've been involved in a federated learning radiology project, the biggest issue is image technique and labelling. Different centers practice very differently, with different imaging protocols, disease prevalences, and labelling/reporting. This project was looking at renal masses and the only part that worked well with federated learning was image segmentation and probability that the mass is a cancer, this is a competency I expect out of a first or second year trainee. It was horrible for predicting subtype of cancer as we couldn't get a good training set (few of these lesions are biopsied, specific MRI sequences that may help are not done the same way in every center) which is what the goal was and more of an experienced generalist/subspecialty radiologist skill. Practically subtype doesn't make too much of a difference for the patient as if it's "probably a cancer" it'll just get cut out anyway, but highlights a challenge with federated learning.
  - chaxor 3y ago
    BERT-like encoder models actually do help here, although unfortunately the dense representation is not as nice and clean as a class, it will at least map to something similar. If the hospital provides even a completely bizarre entity id, the requirement would be to provide the dictionary of descriptions and train the BERT-like model to associate the entity ids and descriptions.
    - haldujai 3y ago
      > BERT-like encoder models actually do help here, although unfortunately the dense representation is not as nice and clean as a class, it will at least map to something similar. Completely agree with this. > If the hospital provides even a completely bizarre entity id This is where it beings to fall apart, the hospital doesn't always provide an entity. In this specific project, some will argue "it's a big enough renal mass just cut it out, based on current evidence it's safest to remove" which while true at the moment doesn't help us in developing a model to prospectively predict the subtypes that do/do not need surgical excision. Consequently, there is no biopsy and specific MRI sequences that are proving helpful at subtyping are missing, irrelevant for clinical care per current standard practice but hugely relevant if you're trying to change that practice as we need labelled data but we don't have the resources to have a radiologist/pathologist go over every case again and try to fill in some blanks en masse.
- wouldbecouldbe 3y ago
  Im not sure it even has to be complicated and full on machine learning. Could also just make it easy to share diagnosis to the a database stripped from personal information about the client, properly index it, share it worldwide, allow doctors to contact eachother automatically who match.
  - distant_hat 3y ago
    If it is a rare condition, reidentifying people from even small amounts of relevant data (age, gender, hospital visited) data is trivial. If it is a common condition, usually there is plenty of data already there.
- renonce 3y ago
  Is that really possible? I don't understand the field exactly but at least if you use deep learning, you'll be able to recover inputs from gradients, and if you want to program it such that no other party is able to recover your inputs, that's called cryptography and homomorphic encryption is FAR from practical yet (like a million times slower than practical). Without rigid mathematical foundations, I would doubt that it's just a fancy way of gathering all the training data and doing the training and making it perceive as if everyone retained their own data.
wouldbecouldbe 3y ago
Yeah you have to make it non-personal, as well as deal with the commercial side of it. I've seen a few initiatives, but they all seem to lack the pockets, drive and ambition of an arrogant Silicon startup. Even though it's annoying at times, it might be what can do the trick in this case.
sampo 3y ago
> That's one of the biggest roadblocks: hospital & healthcare entities will not not have permissible mass sharing of data. Maybe in some other country?
- srvmshr 3y ago
  I am not certain how it works in all of Europe, but it doesn't seem straightforward in Sweden or UK. In US and Japan, they will rather do the full roster of tests than transfer in radiology results from another hospital. Its not something to do with costs, but physician opinions. Every specialist has his personalized view of the patient in hand - so the corpus of information he needs is different.
- brabel 3y ago
  As far as I know, in Sweden data about a patient is shared between every hospital... you need to sign up on a web portal, of course, but from then on, you can authorize a doctor or nurse anywhere to get your data with a click... you can go to any clinic and they have access to your full history. It's impossible to do anything properly if you don't have that. I just don't understand why in the US, it's acceptable for hospitals to be silo-ed like that to the detriment of everyone's health. Here's a news report from 2017, when the system was being set up: https://www.bmj.com/content/357/bmj.j2069 https://www.bmj.com/content/357/bmj.j2069 Today, this has been working for years.
  - hattmall 3y ago
    The US has multiple individual medical systems larger than Sweden with essentially the same abilities. Sweden has roughly the same number of hospitals as South Carolina.
    - brabel 3y ago
      What makes you think the Swedish system wouldn't scale to countries with 100's of millions of people??
      - 3y ago
mtkhaos 3y ago
What's nice is considering a global audience.
dustingetz 3y ago
this may be a sufficiently powerful macro force to disrupt that
weatherlite 3y ago
I think it will happen much faster in China, India or maybe Japan than in the U.S for that exact reason. It's quite possible the U.S will lag a lot in healthcare soon.

srge 3y ago

Exactly: what if my radiologist doesn’t know my particular rare condition? I would feel comfortable removing the human diagnostician. Let’s have actual human doctors acting as researchers working to improve the AI diagnostics.

haldujai 3y ago
My perspective as both a radiologist and CS/AI researcher so exactly what you're proposing: 1. We don't practice in imaginary vacuums, it's easy* to identify that something looks abnormal and then refer to clinical resources/other physicians for rare diseases with specific questions in mind (i.e. recognizing the nuanced imaging finding and referring to a resource to assist). As a rare disease example, tumors of the eyeball/orbit are very rare, but detecting them is not. If I open the case and see one I can refer to StatDx to help me narrow my differential knowing what imaging features I'm looking for. This reduces my misdiagnosis rate (which as an aside is ~2-5% for radiologists, "major" clinically significant that impact morbidity or mortality ~2-10% of those depending on the study). 2. Rare disease are hard to diagnose, and would likely also be hard for an AI. Imaging appearances are not unique to the vast majority of diseases, especially what we cal the "weird and wonderful". Pelvic tuberculosis, endometriosis, advanced cervical cancer and advanced rectal cancer can look identical/nearly identical on MRI and the clinical portion as well as additional testing helps us get to the diagnosis. We don't have to diagnose everything based on a single imaging test, nor should we given: 3. Diagnosis is a tradeoff of sensitivity and specificity. You can't have both. Let's consider adrenal gland tumors. Statistically these are going to be benign, there is no specific imaging feature to tell a small adrenocortical carcinoma (ACC) ~1 in 1 million incidence from an adrenal adenoma (99% of adrenal lesions). We also can't tell them apart with a biopsy under a microscope. If you're unlucky enough to get an ACC you're basically shit out of luck as the only options we have are to recommend adrenal surgery (and their complications which can be death) to optimize sensitivity, or assume that it's benign and optimize for specificity considering disease prevalence and risk of overdiagnosis. In practice, we just use a cutoff of 4cm. I'm not sure how an AI would solve this, especially as there isn't a large enough training set. MD Anderson has the most experience of any center and they've had ~600 cases in 40+ years which as you can imagine encompasses a very heterogeneous imaging set (we didn't have multidetector CT or 3T abdominal MRI 20+ years ago). Overall, AI can and should help radiologists and as someone involved in this field I can't envision a world where we can safely remove the human diagnostician element from the mix, given that it's a spectrum of grey not black/white labelling as it is for object detection. We've had attempts with mammography and stroke AI and it's still horrendously inaccurate compared to what I expect out of a resident radiologist let alone an experience staff physician.

gonzo41 3y ago

You need to pair a system like this with regulation and changes in the medical field to upskill nurses so they have a larger extended skill set and essentially have a path to becoming doctors via practical experience. There's a lot of information on medical conditions. I remember hearing about IPhone apps that can get a 90% hit rate on correct diagnosis for general practitioner style visits. Taking that into account, and a large scale medical database, you'd think we'd be pushing that technology down so we can get better productivity out of primary care, so most of the time Dr's were using their big brains for big complex problems.

wouldbecouldbe 3y ago
90% hit rate is pretty easy to accomplish for a standard doctor's visit, a big percentage of the doctor's visit are standard. You can probably train most humans to deal with that quickly. They often use students if the pressure is high here. It's about the other 10% + unforeseen issues.
- haldujai 3y ago
  Speaking to radiology, ~90% of imaging studies are normal so I can sign my default template without looking at the images and be correct the vast majority of the time.
  - cicce19 3y ago
    Also a rad (and heavily in the AI field). I think you mean 90% have inconsequential findings and not that they are "normal". Can you really apply untouched templates to 90% of your cases? I find that very hard to believe. How many templates do you have and how can they possibly describe the variety of possible findings seen on each type of study?
- taneq 3y ago
  90% hit rate could probably be achieved with "take some panadol and come back in a couple of days if you still feel bad."
3y ago