Researchers have taken photographs of children’s retinas and screened them using a deep learning AI algorithm to diagnose autism with 100% accuracy. The findings support using AI as an objective screening tool for early diagnosis, especially when access to a specialist child psychiatrist is limited.

AI-screened eye pics diagnose childhood autism with 100% accuracy::undefined

    • kromem@lemmy.worldEnglish
      7·
      10 months ago

      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.

    • Lmaydev@programming.devEnglish
      93·
      10 months ago

      A convolutional neural network, a deep learning algorithm, was trained using 85% of the retinal images and symptom severity test scores to construct models to screen for ASD and ASD symptom severity. The remaining 15% of images were retained for testing.

      It correctly identified 100% of the testing images. So it’s accurate.

      • jet@hackertalks.comEnglish
        21·
        10 months ago

        Then somebody’s lying with creative application of 100% accuracy rates.

        The confidence interval of the sequence you describe is not 100%

        • eggymachus@sh.itjust.worksEnglish
          6·
          10 months ago

          From TFA:

          For ASD screening on the test set of images, the AI could pick out the children with an ASD diagnosis with a mean area under the receiver operating characteristic (AUROC) curve of 1.00. AUROC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUROC of 0.0; one whose predictions are 100% correct has an AUROC of 1.0, indicating that the AI’s predictions in the current study were 100% correct. 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.

          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.

    • piecat@lemmy.worldEnglish
      83·
      10 months ago

      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