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.
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.
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.
Sample set of 2
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.
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.
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.
