AI-driven ADHD screening using eye images developed in Korea

A new study from South Korea has demonstrated the use of AI models to possibly diagnose and stratify attention deficit hyperactivity disorder by analysing eye images.

FINDINGS

Researchers from Yonsei University Health System have used four machine learning models and the AutoMorph deep learning pipeline to analyse approximately 1,108 retinal fundus photographs from over 600 children with ADHD and children with typical development. 

Based on findings published in npj Digital Medicine, all four models showed high accuracy, reaching up to 96.9%. These AI models also showed high performance of up to 87.3% in predicting the degree of impairment in visual selective attention, an ability that ADHD patients are usually challenged with. 

The study also identified representative symptoms of ADHD by deriving key retinal features via a Shapley Additive Explanations analysis. These include increased vascular density, decreased arterial vessel width, and changes in the optic disc structure.

WHY IT MATTERS

ADHD is a neurodevelopmental disorder that is difficult to diagnose quickly, says the Yonsei University researchers. Challenges in diagnosis include patients’ high subjectivity, the variability of symptoms among individuals, and overlapping symptoms with other existing conditions.    

Considering the established role of dopamine in retinal function and ADHD symptoms, the researchers looked at retinal images as a potential ADHD screening biomarker in their study.

Eventually, they proved the potential of retinal images as an ADHD biomarker and rapid screening tool.

“Fundus examinations are very simple, taking less than five minutes. It seems these can be used as a rapid test to monitor the effectiveness of ADHD treatments,” said Keun-ah Cheon, professor of Paediatrics at Severance Hospital and co-research lead. 

THE LARGER TREND

Most digital innovations that came out over the past years have only complemented ADHD management, like a mobile application developed at Flinders University in Australia and a digital therapeutics-based program by Singapore’s Institute of Mental Health and local startup Neeuro.   

Meanwhile, eye and eye image-based screening technologies, augmented by AI, have also been applied to diagnose or predict other neurodevelopmental conditions, particularly autism spectrum disorder. Recently, a researcher from Waseda University in Japan demonstrated the use of eye-tracking technology to test children’s responses to predictable movement stimuli, which was found to have potential as a behavioural marker for early autism diagnosis. 

A new study from South Korea has demonstrated the use of AI models to possibly diagnose and stratify attention deficit hyperactivity disorder by analysing eye images.

FINDINGS

Researchers from Yonsei University Health System have used four machine learning models and the AutoMorph deep learning pipeline to analyse approximately 1,108 retinal fundus photographs from over 600 children with ADHD and children with typical development. 

Based on findings published in npj Digital Medicine, all four models showed high accuracy, reaching up to 96.9%. These AI models also showed high performance of up to 87.3% in predicting the degree of impairment in visual selective attention, an ability that ADHD patients are usually challenged with. 

The study also identified representative symptoms of ADHD by deriving key retinal features via a Shapley Additive Explanations analysis. These include increased vascular density, decreased arterial vessel width, and changes in the optic disc structure.

WHY IT MATTERS

ADHD is a neurodevelopmental disorder that is difficult to diagnose quickly, says the Yonsei University researchers. Challenges in diagnosis include patients’ high subjectivity, the variability of symptoms among individuals, and overlapping symptoms with other existing conditions.    

Considering the established role of dopamine in retinal function and ADHD symptoms, the researchers looked at retinal images as a potential ADHD screening biomarker in their study.

Eventually, they proved the potential of retinal images as an ADHD biomarker and rapid screening tool.

“Fundus examinations are very simple, taking less than five minutes. It seems these can be used as a rapid test to monitor the effectiveness of ADHD treatments,” said Keun-ah Cheon, professor of Paediatrics at Severance Hospital and co-research lead. 

THE LARGER TREND

Most digital innovations that came out over the past years have only complemented ADHD management, like a mobile application developed at Flinders University in Australia and a digital therapeutics-based program by Singapore’s Institute of Mental Health and local startup Neeuro.   

Meanwhile, eye and eye image-based screening technologies, augmented by AI, have also been applied to diagnose or predict other neurodevelopmental conditions, particularly autism spectrum disorder. Recently, a researcher from Waseda University in Japan demonstrated the use of eye-tracking technology to test children’s responses to predictable movement stimuli, which was found to have potential as a behavioural marker for early autism diagnosis.