AI-screened eye pics diagnose childhood autism with 100% accuracy::undefined
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.
AI-screened eye pics diagnose childhood autism with 100% accuracy
Submitted 1 year ago by L4s@lemmy.world [bot] to technology@lemmy.world
https://newatlas.com/medical/retinal-photograph-ai-deep-learning-algorithm-diagnose-child-autism/
Comments
lhx@lemmy.world 1 year ago
kromem@lemmy.world 1 year ago
Retinal photographs of individuals with ASD were prospectively collected between April and October 2022, and those of age- and sex-matched individuals with TD were retrospectively collected between December 2007 and February 2023.
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:
Considering that a positive correlation exists between retinal nerve fiber layer (RNFL) thickness and the optic disc area,32,33 previous studies that observed reduced RNFL thickness in ASD compared with TD14-16 support the notable role of the optic disc area in screening for ASD. Given that the retina can reflect structural brain alterations as they are embryonically and anatomically connected,12 this could be corroborated by evidence that brain abnormalities associated with visual pathways are observed in ASD. First, reduced cortical thickness of the occipital lobe was identified in ASD when adjusted for sex and intelligence quotient.34 Second, ASD was associated with slower development of fractional anisotropy in the sagittal stratum where the optic radiation passes through.35 Interestingly, structural and functional abnormalities of the visual cortex and retina have been observed in mice that carry mutations in ASD-associated genes
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.
pelespirit@sh.itjust.works 1 year ago
I’m honestly not sure if this whole thing is a good thing or a freaking scary thing.
At the back of the eye, the retina and the optic nerve connect at the optic disc. An extension of the central nervous system, the structure is a window into the brain and researchers have started capitalizing on their ability to easily and non-invasively access this body part to obtain important brain-related information.
eggymachus@sh.itjust.works 1 year ago
Column A: yes Column B: also yes
AlfredEinstein@lemmy.world 1 year ago
I guess it’s time to genocide the normies. ¯\_(ツ)_/¯
kromem@lemmy.world 1 year ago
It’s way less scary in the actual linked paper:
Given that the retina can reflect structural brain alterations as they are embryonically and anatomically connected,12 this could be corroborated by evidence that brain abnormalities associated with visual pathways are observed in ASD.
TLDR: Abnormal developments in the brain that have visual components may closely correlate with abnormal developments in the eye.
Vibi@lemmy.world 1 year ago
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.
cynar@lemmy.world 1 year ago
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.
kromem@lemmy.world 1 year ago
I hope it can be applied to any age.
According to the study:
Our sequential age-based modeling suggested that retinal photographs may serve as an objective screening tool starting at least at age 4 years. Moreover, the newborn retina continues to develop and mature up to age 4 years.44,45 Taken together, our models are potentially viable for screening children from this age onward, which is earlier than the average age of 60.48 months at ASD diagnosis.
So not any age, but fairly early on.
SpaceNoodle@lemmy.world 1 year ago
Hold the fuck up. What exactly is the marker?
Lmaydev@programming.dev 1 year ago
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.
partial_accumen@lemmy.world 1 year ago
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.
kromem@lemmy.world 1 year ago
Not so much of a mystery:
There was no notable decrease in the mean AUROC, even when 95% of the least important areas of the image – those not including the optic disc – were removed.
So we know that it relates to the optic disc.
CluckN@lemmy.world 1 year ago
Sample set of 2
sndrtj@feddit.nl 1 year ago
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.
just_another_person@lemmy.world 1 year ago
Bull.Shit.
girsaysdoom@sh.itjust.works 1 year ago
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.
eggymachus@sh.itjust.works 1 year ago
The article seems to be published in JAMA network open, and as far as I can tell that publication is peer reviewed?
just_another_person@lemmy.world 1 year ago
Yeah, read it. No other confirmation.
kromem@lemmy.world 1 year ago
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.