Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification

While neuroimaging offers valuable insights into brain alterations associated with psychiatric disorders, its translation into routine clinical practice remains challenging. we developed the patch-based hierarchical network (PHN), a deep learning framework for classifying multiple psychiatric disorders from structural MRI. Trained on a large, curated dataset (<i>n</i> = 2,490) of four major disorders and controls, the PHN's generalizability was confirmed on independent research datasets (<i>n</i> = 1,346) and real-world clinical data (<i>n</i> = 344). The model demonstrated robust performance, showing promise in real-world evaluations by reflecting complex presentations such as comorbidities. A key contribution is the implementation and deployment of a closed-loop, neuroimaging-based diagnostic support system integrating the PHN into an active clinical workflow. This work provides a tangible step toward bridging the research-to-practice gap, leveraging artificial intelligence to provide objective support for psychiatric diagnosis.

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Publication Details

Journal
iScience
Published
2025-11-20
DOI
https://doi.org/10.1016/j.isci.2025.114100
Primary Topic
Functional Brain Connectivity Studies
Type
article
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article

Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification

Dinggang G. Shen, Jin Cheng, Kaini Qiao, Wengzheng Wang et al.
iScience
Functional Brain Connectivity Studies
article

Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification

Dinggang G. Shen, Jin Cheng, Kaini Qiao, Wengzheng Wang, Jianhua Sheng, Qing Fan, Yingying Tang, Qingfeng Li, Daihui Peng, Yiru Fang, Margaret Niznikiewicz, Zhi Yang, Chunbo Li, Zhen Wang, Xiaochen Zhang, Guanjun Li, Lijuan Jiang, Hong Qiu, Min Zhao, Qian Guo, Jinhong Wang, Yang Hu, Jijun Wang
article en

Abstract

While neuroimaging offers valuable insights into brain alterations associated with psychiatric disorders, its translation into routine clinical practice remains challenging. we developed the patch-based hierarchical network (PHN), a deep learning framework for classifying multiple psychiatric disorders from structural MRI. Trained on a large, curated dataset (<i>n</i> = 2,490) of four major disorders and controls, the PHN's generalizability was confirmed on independent research datasets (<i>n</i> = 1,346) and real-world clinical data (<i>n</i> = 344). The model demonstrated robust performance, showing promise in real-world evaluations by reflecting complex presentations such as comorbidities. A key contribution is the implementation and deployment of a closed-loop, neuroimaging-based diagnostic support system integrating the PHN into an active clinical workflow. This work provides a tangible step toward bridging the research-to-practice gap, leveraging artificial intelligence to provide objective support for psychiatric diagnosis.

iScienceVol. 28(12)
Shanghai University (CN), Harvard University (US), Shanghai University of Engineering Science (CN), University of Shanghai for Science and Technology (CN), Shanghai Jiao Tong University (CN), Capital Medical University (CN), Shanghai Mental Health Center (CN), ShanghaiTech University (CN), Beijing Anding Hospital (CN), United Imaging Healthcare (China) (CN)
Openalex Percentile: Top 65%
Functional Brain Connectivity Studies
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