Introduction to Autism Detection Project
A consortium led by South Korea’s top universities, Yonsei University and Seoul National University, will embark on a national project promoting the early detection of autism spectrum disorder in the country. Backed by the National Center for Mental Health and the Ministry of Health and Welfare, the project received 9.2 billion won ($6.6 million) in government funding.
Project Goals and Objectives
The project aims to develop AI-powered digital and medical devices for screening autism, which are targeted for release by 2028. The devices will be run by algorithms based on data from a cohort of 1,200 infants and toddlers below 48 months. Additionally, applications for an innovative medical device designation from the Ministry of Food and Drug Safety will be pursued.
Why Early Detection Matters
A recent article noted an overall increase in cases of ASD among Korean children and adolescents from 2011 to 2021. It has been estimated that the prevalence of DSM-5-defined ASD is at 2.20%. This trend may necessitate calibrating strategies to enable timelier and more effective prevention and care of children with autism. However, current approaches in ASD diagnosis are largely dependent on specialists’ findings and subjective observations of guardians, which often render them inconsistent and short of objectivity.
The Role of AI in Autism Detection
"The AI-based [ASD] screening assistive medical device to be developed through this project will be an opportunity to shift the paradigm of early autism diagnosis and treatment," explained Chun Geun-ah, professor at Yonsei University Severance Hospital and principal project investigator. "The consortium will establish a foundation to overcome the limitations of diagnosing [ASD] and lead to better treatment outcomes," Kim Boong-nyeon, SNU Hospital (SNUH) professor and co-principal investigator, also said.
Consortium and Partners
The consortium involves several major hospitals, such as Yonsei’s Gangnam Severance Hospital, Ewha Womans University Hospital in Seoul and Mokdong, and Seoul St. Mary’s Hospital, and digital health companies, including HurayPositive and Adotcure.
The Larger Trend
In 2023, SNUH opened a living laboratory to collect data that will be used to develop algorithm-driven models for detecting and personalising the treatment of ASD. The lab, also supported by the NCMH, serves as a bedrock for new digital therapeutics and the discovery of autism biomarkers. AI has seen increasing applications in ASD screening and treatment. EarliTec Diagnostics in the United States is a major developer in this space. It received regulatory approval for its autism decision support tool in 2022. Meanwhile, in Japan, new research utilised eye-tracking technology to demonstrate how predictable movement stimuli can be potentially used as a behavioural marker for autism.
Conclusion
The project led by Yonsei University and Seoul National University is a significant step towards early detection and treatment of autism spectrum disorder in South Korea. With the help of AI-powered digital and medical devices, the consortium aims to improve the diagnosis and treatment outcomes for children with autism. As research and development in this area continue to grow, we can expect to see more innovative solutions for autism detection and treatment.
FAQs
- What is the goal of the project led by Yonsei University and Seoul National University?
The goal of the project is to develop AI-powered digital and medical devices for screening autism. - How much funding did the project receive from the government?
The project received 9.2 billion won ($6.6 million) in government funding. - What is the estimated prevalence of DSM-5-defined ASD in Korean children and adolescents?
The estimated prevalence of DSM-5-defined ASD is at 2.20%. - What is the role of AI in autism detection?
AI can help improve the diagnosis and treatment outcomes for children with autism by providing more accurate and objective screenings. - What are some examples of innovative solutions for autism detection and treatment?
Examples include the use of eye-tracking technology and algorithm-driven models for detecting and personalising the treatment of ASD.
