认知
人工智能
深度学习
桥接(联网)
机器学习
集合(抽象数据类型)
心理学
精神病
计算机科学
精神分裂症(面向对象编程)
认知心理学
精神科
计算机网络
程序设计语言
作者
Yang Wen,Chuan Zhou,Leiting Chen,Yu Deng,Martine Cleusix,Raoul Jenni,Philippe Conus,Kim Q.,Lijing Xin
标识
DOI:10.3389/fpsyt.2022.1075564
摘要
Introduction Recent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less accurate on early psychosis (EP) since there are mild structural brain changes at early stage. As cognitive impairments is one main feature in psychosis, in this study we apply a multi-task deep learning framework using sMRI with inclusion of cognitive assessment to facilitate the classification of patients with EP from healthy individuals. Method Unlike previous studies, we used sMRI as the direct input to perform EP classifications and cognitive estimations. The proposed deep learning model does not require time-consuming volumetric or surface based analysis and can provide additionally cognition predictions. Experiments were conducted on an in-house data set with 77 subjects and a public ABCD HCP-EP data set with 164 subjects. Results We achieved 74.9 ± 4.3% five-fold cross-validated accuracy and an area under the curve of 71.1 ± 4.1% on EP classification with the inclusion of cognitive estimations. Discussion We reveal the feasibility of automated cognitive estimation using sMRI by deep learning models, and also demonstrate the implicit adoption of cognitive measures as additional information to facilitate EP classifications from healthy controls.
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