Poster No:
921
Submission Type:
Abstract Submission
Authors:
Jungha Lee1, Minah Kim2, Soo Yong Lee3, Minji Ha1, Inkyung Park4, Youngeun Cho5, Moonyoung Jang2, Sunghyun Park2
Institutions:
1Seoul National University, Seoul, Seoul, 2Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Seoul, 3KAIST, Daejeon, Daejeon, 4Seoul National University, Seoul, -, 5Seoul National University, Seoul , Seoul
First Author:
Co-Author(s):
Minah Kim
Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Seoul
Minji Ha
Seoul National University
Seoul, Seoul
Moonyoung Jang
Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Seoul
Sunghyun Park
Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Seoul
Introduction:
Executive function and visuospatial memory are often impaired in patients with psychosis and obsessive-compulsive disorder (OCD), and other various psychiatric and neurological disorders. To directly and rapidly assess such deficits linking them to underlying brain dysfunctions, a biomarker-based assessment is needed, with potential applications to a wider range of disorders. This study aimed to develop a foundational eye-tracking-based deep learning model using the Rey-Osterrieth Complex Figure Test (RCFT) to assess executive function during visuospatial memory encoding. Initially devised from patients with early psychosis, OCD, and healthy controls (HCs), with potential for expansion to other psychiatric and neurological disorders.
Methods:
Eye-tracking was conducted in 56 patients with first-episode psychosis (FEP), 26 subjects at clinical high risk for psychosis (CHR), 70 OCD patients, and 125 HCs during a 3-minute RCFT figure memorization. The fixation points, where the eyes focus on specific areas, were converted into scanpath images, training Convolutional Neural Networks (CNN) to extract visuospatial eye movement patterns alongside conventional RCFT organization scores for executive function and immediate recall scores for visuospatial memory. The raw fixation points were also used to train Long-Short-Term-Memory (LSTM) to extract sequential eye movement patterns, using the same conventional RCFT scores.
Results:
The CNN model accurately predicted executive function (F1 score: 95.65%) and visual memory (F1 score: 92.5%) for superior and impaired levels. The LSTM model accurately predicted executive function (F1 score: 81.19%) and visual memory (F1 score: 94.06%) for superior and impaired levels in early psychosis and OCD, regardless of their disease diagnosis.
Conclusions:
These findings suggest that the eye-tracking and deep learning based RCFT model can provide a more direct and rapid assessment tool for executive function in visuospatial memory encoding, with potential future applications to other psychiatric and neurological disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Learning and Memory:
Learning and Memory Other
Modeling and Analysis Methods:
Classification and Predictive Modeling
Keywords:
Cognition
Machine Learning
Memory
Neurological
Obessive Compulsive Disorder
Psychiatric Disorders
Schizophrenia
Other - Eyetracking; deep learning
1|2Indicates the priority used for review
Provide references using author date format
Kim, M.-S., Namgoong, Y., & Youn, T. (2008). Effect of organizational strategy on visual memory in patients with schizophrenia. Psychiatry and Clinical Neurosciences, 62(4), 427–434. https://doi.org/10.1111/j.1440-1819.2008.01821.x
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