Science Alert carries news of a Korean research project that used AI to study images of the fundus, an area of the back of the eye, to successfully pinpoint patients with attention-deficit hyperactivity disorder:
Of four machine learning models tested in the study, the best achieved a 96.9 percent score for predicting ADHD accurately, based on image analysis alone.
The team found that higher blood vessel density, shape and width of vessels, and certain changes in the eye’s optic disc were key signs someone had the condition.
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The approach was tested on 323 children and adolescents already diagnosed with ADHD, and another 323 without an ADHD diagnosis, matched by age and sex to the first group.
The researchers found the AI system scored highly across several measures when it came to predicting ADHD. It also performed well at spotting some of the characteristics of the disorder, including impairments in visual selective attention.
Several machine learning techniques to screen for ADHD have been explored recently, from analysis of alternative eye scans to behavioral tests, but this one has a few major drawcards. While not the absolute most accurate method in terms of raw scores, it’s very close, it’s also quick to run and assess, and simple to scale up.
“Notably, earlier high-accuracy models typically relied on a diverse set of variables, each contributing incrementally to differentiating subjects,” write the researchers.
“Our approach simplifies the analysis by focusing exclusively on retinal photographs. This single-source data strategy enhances the clarity and utility of our models.”
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You can read more about the retinal research here, in npj Digital Medicine.