How artificial intelligence is transforming methods for the early detection of autism
This is a virtual seminar. For a Zoom link, please see "Venue". Please consider subscribing to mailing list: web.maillist.ox.ac.uk/ox/subscribe/ai4mch
Early detection of autism is an essential first step toward access to intervention and services, which can improve quality of life and long-term outcomes. Although autism screening questionnaires are useful, they require literacy and have lower accuracy in real-world settings. Thus, there remains a need for feasible, accurate, and scalable methods for directly observing and quantifying early signs of autism. We have validated a digital phenotyping application (SenseToKnow) which can be remotely administered on a smartphone or tablet and uses computer vision and machine learning to accurately detect autism in young children. This presentation will discuss how SenseToKnow and other computational approaches based on machine learning are transforming methods for the early detection of autism.
Date:
11 February 2025, 15:00
Venue:
https://zoom.us/j/92860307789?pwd=iAdkC3QG1wQ8yvbuOBFTibGofmszPY.1
Speaker:
Professor Geraldine Dawson (Duke University)
Organising department:
Department of Psychiatry
Organiser:
Dr Andrey Kormilitzin (University of Oxford)
Organiser contact email address:
andrey.kormilitzin@psych.ox.ac.uk
Host:
Dr Andrey Kormilitzin (University of Oxford)
Part of:
Artificial Intelligence for Mental Health Seminar Series
Booking required?:
Not required
Booking url:
https://web.maillist.ox.ac.uk/ox/info/ai4mch
Booking email:
andrey.kormilitzin@psych.ox.ac.uk
Audience:
Public
Editor:
Andrey Kormilitzin
