AI-screened eye pics diagnose childhood autism with 100% accuracy
(newatlas.com)
from L4s@lemmy.world to technology@lemmy.world on 18 Dec 2023 20:00
https://lemmy.world/post/9724922
from L4s@lemmy.world to technology@lemmy.world on 18 Dec 2023 20:00
https://lemmy.world/post/9724922
AI-screened eye pics diagnose childhood autism with 100% accuracy::undefined
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Hold the fuck up. What exactly is the marker?
A big problem with this type of ai is they are a black box.
We don’t know what they are identifying. We give it input and it gives output. What exactly is going on internally is a mystery.
Counterintuitively that’s also where the benefit comes from.
The reason most AI is powerful isn’t because its can think like humans, its because it doesn’t. It makes associations that humans don’t simply by consumption of massive amounts of data. We humans tell it “Here’s a bajillion sample examples of X. Okay, got it? Good. Now here’s 10 bajillion samples we don’t know if they are X or not. What do you, AI, think?”
AI isn’t really a causation machine, but instead a correlation machine. The AI output effectively says “This thing you gave me later has some similarities to the thing you gave me before. I don’t know if the similarities mean anything, but they ARE similarities”.
Its up to us humans to evaluate the answer AI gave us, and determine if the similarities it found are useful or just coincidental.
Sure, but if we could take the model generated by the AI and convert it into a set of quantifiable criteria - i.e., what is being correlated - we could use our human abilities of associative thought to gain an understanding of why this correlation may exist, possibly leading to better understanding of Autism overall.
The problem is identifying what an AI model is doing is basically impossible. You can’t just decompile an AI model and see a bunch of logic, and you can’t view the machine code and reverse engineer it because it isn’t code in that sense. The best way to suss it out is to throw corner cases at it and try to figure out any common themes in the false negatives and false positives
No, we just haven’t come up with a way of reverse-engineering AI models yet.
Incidentally to train AI, you need a bajillion samples of X and a bajillion-plus samples of not-X.
Not so much of a mystery:
So we know that it relates to the optic disc.
Edit: Repeated in the conclusions of the study itself:
Edit 2: Which is given more background as to what may be going on and being picked up by the model:
Bull.Shit.
Define the criteria, have it peer reviewed and diagnosed, or else we will ALL be diagnosed with Autism soon enough.
The article seems to be published in JAMA network open, and as far as I can tell that publication is peer reviewed?
Yeah, read it. No other confirmation.
But it has been peer reviewed? And the criteria have been defined?
Read the article. This is a link generator. No link to a peer reviewed paper.
This is linked: jamanetwork.com/journals/…/2812964?utm_source=For…
For real.
It looks like the actual number of candidates were 958 and only 15% of that number were reserved for testing, the rest were used in AI training data. So in reality only 144 people were tested with the AI and there’s no information from the article on how many people were formally diagnosed of this subset.
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At the bottom of the article, the paper has been published in a peer reviewed journal.
jamanetwork.com/journals/…/2812964
You can’t just believe something because it’s been peer-reviewed. It is an absolutely minimal requirement for credibility these days but the system does not work well at all.
In this case, the authors acknowledge the need for more studies to establish how generalisable their findings are. It’s the first attempt at building a tool, it doesn’t mean anything at all until the findings are reproduced by an independent group.
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Totally agree, like for those vaccins. It’s not because they are published they are safe ! /s.
Sidebar: this talk of papers reminded me of <img alt="writing one when hungry" src="https://lemmy.world/pictrs/image/5c2637b0-348b-4750-8e75-d15c138547a9.webp">
Isn’t it a part of what someone printed on their neighbor’s wifi printer ?
totally agree, peer reviewing is the bare minimum, but it IS a step above any old article published on a random website. also would like to acknowledge the limitations of this particular study. fair criticism and is something the authors brought up in their paper too.
my reply was in response to the original commenter mentioning that there was no link to the study at all.
They do point to where the model was making its decision based off of, which was the optical disc, which they go over in the discussion with multiple previous studies showing biological differences between ASD and TD development.
You know, in the peer reviewed paper linked at the bottom of OP’s article on it.
100% ? That’s a fucking lie. Nothing in life is 100%
It correctly identified 100% of the testing images. So it’s accurate.
Then somebody’s lying with creative application of 100% accuracy rates.
The confidence interval of the sequence you describe is not 100%
From TFA:
They at least define how they get the 100% value, but I’m not an AIologist so I can’t tell if it is reasonable.
100% accuracy is troublesome. Literally statistics 101 stuff, they tell you in no uncertain terms, never, never trust 100% accuracy.
You can be certain to some value of p. That number is never 0. .001 is suspicious as fuck, but doable. .05 is great if you have a decent sample size.
They had fewer than 1000 participants.
I just don’t trust it. Neither should they. Neither should you. Not at least until someone else recreates the experiments and also finds this AI to be 100% accurate.
What they’re saying, as far as I can tell, is that after training the model on 85% of the dataset, the model predicted whether a participant had an ASD diagnosis (as a binary choice) 100% correctly for the remaining 15%. I don’t think this is unheard of, but I’ll agree that a replication would be nice to eliminate systemic errors. If the images from the ASD and TD sets were taken with different cameras, for instance, that could introduce an invisible difference in the datasets that an AI could converge on. I would expect them to control for stuff like that, though.
You need to report two numbers for a classifier, though. I can create a classifier that catches all cases of autism just by saying that everybody has autism. You also need a false positive rate.
True, but as far as I can tell the AUROC measure they refer to incorporates both.
Yup, you’re right, good catch 🙂
What was the problem with that male vs female deep-learning test a few years ago?
That all the males were earlier in the day, so the sun angle in the background was a certain direction, while all the females were later in the day, so the sun was in a different angle? And so it turned out that the deep-learning AI was just trained on the window in the background?
100% accuracy almost certainly means this kind of effect happened. No one gets perfect, all good tests should be at least a “little bit” shoddy.
Definitely possible, but we’ll have to wait for some sort of replication (or lack of) to see, I guess.
Yeah, exactly. They’re reporting findings. Saying that it worked in 100% of the cases they tested is not making a claim that it will work in 100% of all cases ever. But if they had 30 images and it classified all 30 images correctly, then that’s 100%.
The article headline is what’s misleading. First, it’s poorly written - “AI-screened eye PICS DIAGNOSE childhood autism.” The pics do not diagnose the autism, so the subject of the verb is wrong. But even if it were rephrased, stating that the AI system diagnoses autism itself is a stretch. The AI system correctly identified individuals previously diagnosed with autism based on eye pictures.
This is an interesting but limited finding that suggests AI systems may be capable of serving as one diagnostic tool for autism, based on one experiment in which they performed well. Anything more than that is overstating the findings of the study.
They talk about collecting the images - the two populations of images were collected separately. It’s probably not 100% of the difference, but it might have been enough to push it up to 100%
You mean like the infamous AI model for detecting skin cancers that they figured out was simply detecting if there’s a ruler in the photo because in all of the data fed into it the skin cancer photos had rulers and the control images did not
Yeah, from the way they wrote, it sounds to me they indirectly trained on the test set
Except death and taxes
Are you 100% sure of that?
Not even your statement?
Could we reasonably expect an AI to something right 100% if a human could do it with 100%?
Could you tell if someone has down syndrome pretty obviously?
Maybe some kind of feature exists that we aren’t aware of
Other aspects weren’t 100%, such as identifying the severity (which was around 70%).
But if I gave a model pictures of dogs and traffic lights, I’d not at all be surprised if that model had a 100% success rate at determining if a test image was a dog or a traffic light.
And in the paper they discuss some of the prior research around biological differences between ASD and TD ocular development.
Replication would be nice and I’m a bit skeptical about their choice to use age-specific models given the sample size, but nothing about this so far seems particularly unlikely to continue to show similar results.
I’m honestly not sure if this whole thing is a good thing or a freaking scary thing.
Column A: yes
Column B: also yes
I guess it’s time to genocide the normies. ¯\_(ツ)_/¯
It’s way less scary in the actual linked paper:
TLDR: Abnormal developments in the brain that have visual components may closely correlate with abnormal developments in the eye.
This is great. Article explains the method and sample size. This could be a great tool, and I hope it can be applied to any age. Many people who are on the spectrum and are high functioning can go most of their lives without a diagnosis while struggling to understand why the world feels so different to them.
According to the study:
So not any age, but fairly early on.
This is particularly useful, since it would be easy to mass deploy. A quick photo, during a childhood checkup, and it can be easily checked. It doesn’t need to be focused, so could catch a lot more, less obvious cases.
As an autistic myself, an early diagnosis would have potentially helped a lot. This would still be true of those who mask well.
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It’s apparently good at 100% at classifying autism in groups that have already been flagged for high chance of ASD. It is not good at just any old picture.
TD stands for “typical development.”
So it correctly differentiated between children diagnosed with ASD and those without it with 100% accuracy.
The confounding factors are that they excluded children with ASD and other issues that might have muddied the waters, so it may not be 100% effective at distinguishing between all cases of ASD vs TD.
There’s no reason to think that given a retinal photograph of someone who hasn’t been diagnosed with ASD that it would fail to reject the diagnosis or confirm it if ASD was the only factor.
And this appears to be based on biological differences that have already been researched:
And given that the heat maps of what the model was using to differentiate were almost entirely the optical disc, I’m not sure why so many here are scoffing at this result.
It wasn’t 100% at identifying severity or more nuanced differences, but was able to successfully identify whether the retinal image was from someone diagnosed with ASD or not with 100% success rate in the roughly 150 test images split between the two groups.
Sensitivity or specificity? Sensitivity is easy, just say every person is positive and you’ll find 100% of true positives. Specificity is the hard problem.