AIM in Eating Disorders

Publication Type Book Section
Authors David Kopyto, Lena Uhlenberg, Rui Zhang, Valeska Stonawski, Stefanie Horndasch, Oliver Amft
Title AIM in Eating Disorders
Abstract Over the past decades, the burden of eating disorders (ED) and comorbidities
increased worldwide. Assisting diet monitoring with AI methods and Automated
Dietary Montoring (ADM) can support ED risk prediction, diagnosis, tracking associated
symptoms, and medical guidance during a long-term behaviour change
process. This chapter gives an overview of important directions in AI in the field
of EDs and obesity. State-of-the-art methods and technologies for ADM are summarised
in connection to digital biomarkers that reflect diet-related behaviour in
general. Two sensor-based ADM examples are detailed: food type classification and
eating timing estimation. On the example of anorexia nervosa (AN), diet-related psychological
parameters are detailed and AI-based approaches to supportANdiagnosis
and treatment are described.
Book Title Artificial Intelligence in Medicine
Edition Living reference work
Place Cham
Publisher Springer International Publishing
Date 2021
ISBN 978-3-030-58080-3
URL Publisher's website
Full Text PDF
Friedrich-Alexander-Universität Erlangen-Nürnberg