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AI models that predict disease are not as accurate as reports might suggest

scientificamerican.com

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Re: AI models that predict disease are not as accurate as reports might suggest

#111

Post by steveBK123 »

ggm wrote: post #49
lostlogin wrote: post #32

Earlier quoted context omitted.

I work in radiology with MRI as a tech. We use AI slightly differently to the examples here, but it’s changing a lot of what we do. It’s more about enhancing images than directly about diagnosing. The image is denoised ‘intelligently’ in k-space and then the resolution is doubled via another AI process in the image domain (or maybe the resolution is quadrupled, as it depends on how you measure it. Our pixel count dou…

My partner had a clinician review her paperwork and say "why are you here" explaining the enhanced imaging was leading to tentative concerns being raised about structural change so small it was below the threshold for safe surgical treatment. Moral of the story: the imaging has got so good that diagnostics is now on the fringe of over diagnosing and the stats need to catch up

I think it cuts both ways though, over-testing vs under-testing, as the question is when do people actually get access to imaging, and is there more pro-active imaging screening we should have done.

A good friend recently had an unrelated routine surgical procedure go awry, that lead to a CT scan to check on the damage. The CT scan ended up finding stage 2 cancer, larger than a billiard ball, in an organ that is going to be surgically removed. Our friend had absolutely no symptoms of any kind related to the cancer. There is no reason he would have gotten a CT scan other than the unrelated surgical accident. Imagine in 5 years he finally had had some symptoms, they do the scan & and find its stage 4, sorry.

The fact that we only have routine screening regiments for a handful of cancers (breast, colon, prostate, skin) is something that I've been thinking about a lot lately.

Re: AI models that predict disease are not as accurate as reports might suggest

#112

Post by aix1 »

lostlogin wrote: post #108
p1necone wrote: post #92

Earlier quoted context omitted.

AI denoising may be making information that actually is in the image easier to spot, but AI upscaling is just inventing detail that doesn't actually exist in the source image, which seems rather dangerous for this use case.

I disagree. It’s ‘only’ converting each pixel into 4, so the starting point is not going to change a lot. Also keep in mind that interpolating has been part of image reconstructions for 20+ years. MR suffers from slow acquisition times so we cheat and image resolution is rarely symmetrical in pixel dimensions when you compare x, y and z directions. Previously we just made pixels square (made up data) then doubled the…

> We are seeing things we didn’t previously, particularly with cartilage injuries.

I am curious about the process by which you ensure that the things you see are actually there and are not side effects of the enhancement?

Re: AI models that predict disease are not as accurate as reports might suggest

#113

Post by ggm »

steveBK123 wrote: post #111
ggm wrote: post #49

Earlier quoted context omitted.

My partner had a clinician review her paperwork and say "why are you here" explaining the enhanced imaging was leading to tentative concerns being raised about structural change so small it was below the threshold for safe surgical treatment. Moral of the story: the imaging has got so good that diagnostics is now on the fringe of over diagnosing and the stats need to catch up

I think it cuts both ways though, over-testing vs under-testing, as the question is when do people actually get access to imaging, and is there more pro-active imaging screening we should have done. A good friend recently had an unrelated routine surgical procedure go awry, that lead to a CT scan to check on the damage. The CT scan ended up finding stage 2 cancer, larger than a billiard ball, in an organ that is goin…

Yes. And the balance between under testing and some loss of early treatable detection and over testing with some incurring unneeded operations is a hard one. Everyone tends to the over test side. For some things like knee operations the evidence appears strong that surgery is the worst path to take in most cases. Surgery stems from improving imaging of knee joint tissue. Treatment regimes need to catch up to return to a sweet spot of detection and remediation.

My partner feels her breast tissue calcification detected in improving imaging of annual checks should have been left alone and incurred discomfort and scarring she didn't need, but we both know breast cancer survivors who owe their life to detection and intervention

Re: AI models that predict disease are not as accurate as reports might suggest

#114

Post by tablespoon »

dekhn wrote: post #38
srvmshr wrote: post #34

I worked in healthcare ML solutions, as part of my PhD & also as consultant to a telemedicine company. My experience in dealing with data (we had sufficient, and somewhat well labeled) & methods made me realize that a lot of the prediction human doctors make are multimodal - and that is something deep learning will struggle for the time being. For example, say in detection of a disease X , physicians factor in blood…

are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.

> I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.

I suspect software engineers massively underestimate the value of skills outside their domain.

Re: AI models that predict disease are not as accurate as reports might suggest

#115

Post by kqr »

dekhn wrote: post #38
srvmshr wrote: post #34

I worked in healthcare ML solutions, as part of my PhD & also as consultant to a telemedicine company. My experience in dealing with data (we had sufficient, and somewhat well labeled) & methods made me realize that a lot of the prediction human doctors make are multimodal - and that is something deep learning will struggle for the time being. For example, say in detection of a disease X , physicians factor in blood…

are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.

I wouldn't call it massively underestimate -- if I recall the research of Meehl et al correctly, clinicians, on most types of cases considered independently, underperform simple arithmetical models of 2--5 variables by something on the order of 10 %. So not a huge effect, but also humans aren't as good as they think. (They do get lucky though! Sometimes some people get very lucky and accidentally get a long string of cases right.)

Re: AI models that predict disease are not as accurate as reports might suggest

#116

Post by johannes_ne »

roflyear wrote: post #83
johannes_ne wrote: post #8

I recently published a paper, where we explain how an FDA approved prediction model, build into a widely used cardiac monitor was developed with an incredibly biased method. https://doi.org/10.1097/ALN.0000000000004320 Basically, the training and validation data was engineered so an important range for one of the predictor variables was only present in one of the outcomes, making perfect prediction possible for these…

This has to be intentional no?

The problem is quite subtle, though obvious in retrospect. I've seen a paper from a separate, academic, research group make similar model with the exact same problem.

The problem would, however, have been clear, if the model was compared to simply using the current mean blood pressure (MAP) as a predictor of hypotension, because MAP is the problematic predictor variable. Instead, the model was only compared to short-term changes in MAP (ΔMAP), which is obviously nonsensical and has an AUROC of ~0.55.

Re: AI models that predict disease are not as accurate as reports might suggest

#117

Post by johannes_ne »

mjthrowaway1 wrote: post #91
johannes_ne wrote: post #23

Earlier quoted context omitted.

Fair question. The model we comment on both suffer from the problem described in the article but also a more severe problem: The developers sampled obvious cases og hypotension and nonhypotension, and trained the model to distinguish those. And also validated it on data that was similarly dichotomous. In reality the outcome is often between these two scenarios. But worse, they also introduce a more severe problem whe…

I quit research forever after I was ignored pointing out a similar problem in our predictive model.

I can only imagine the frustration. Just getting this through peer-review took half a year, but at least there was the academic currency of a publication to motivate me.

Re: AI models that predict disease are not as accurate as reports might suggest

#118

Post by lostlogin »

aix1 wrote: post #112
lostlogin wrote: post #108

Earlier quoted context omitted.

I disagree. It’s ‘only’ converting each pixel into 4, so the starting point is not going to change a lot. Also keep in mind that interpolating has been part of image reconstructions for 20+ years. MR suffers from slow acquisition times so we cheat and image resolution is rarely symmetrical in pixel dimensions when you compare x, y and z directions. Previously we just made pixels square (made up data) then doubled the…

> We are seeing things we didn’t previously, particularly with cartilage injuries. I am curious about the process by which you ensure that the things you see are actually there and are not side effects of the enhancement?

The same way we do for everything. We scan in multiple planes and image weighting’s (t1, t2fs, etc). Ax, sag cor. We do other angles for various things too. Eg for knees we do dedicated views for the patella cartilage and ACL.

Re: AI models that predict disease are not as accurate as reports might suggest

#119

Post by tgv »

tensor wrote: post #15

This is entirely unsurprising and has a very simple solution: keep adding more data. Our measurements of the accuracy of AI systems are only as good as the test data, and if the test data is too small, then the reported accuracies won't reflect the true accuracies of the model applied to wild data. Basically, we need an accurate measure of whether the test data set is statistically representative of wild data. In hea…

You don't know which data to add.

> statistically representative of wild data

It should be "statistically representative" wrt to the true causes, and all other factors should be independent. Instead, ML models, and certainly large NNs, allow every bit of data that correlates a tiny bit to contribute.

Since we don't know what the true causes are, nor how to represent them in the data, adding more data might just as well not work.

Re: AI models that predict disease are not as accurate as reports might suggest

#120

Post by [deleted] »

aix1 wrote: post #112
lostlogin wrote: post #108

Earlier quoted context omitted.

I disagree. It’s ‘only’ converting each pixel into 4, so the starting point is not going to change a lot. Also keep in mind that interpolating has been part of image reconstructions for 20+ years. MR suffers from slow acquisition times so we cheat and image resolution is rarely symmetrical in pixel dimensions when you compare x, y and z directions. Previously we just made pixels square (made up data) then doubled the…

> We are seeing things we didn’t previously, particularly with cartilage injuries. I am curious about the process by which you ensure that the things you see are actually there and are not side effects of the enhancement?

[deleted]
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