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Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Front Psychol 2024; 15:1375294. [PMID: 38515973 PMCID: PMC10955080 DOI: 10.3389/fpsyg.2024.1375294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/23/2024] Open
Abstract
Objective College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health. Methods Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends. Results Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications. Conclusion This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.
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Affiliation(s)
- Jing Chen
- Wuhan University China Institute of Boundary and Ocean Studies, Wuhan, China
| | - Dongfeng Yuan
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Ruotong Dong
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Jingyi Cai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Zhongzhu Ai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Wuhan, China
| | - Shanshan Zhou
- Hubei Shizhen Laboratory, Wuhan, China
- The First Clinical Medical School, Hubei University of Chinese Medicine, Wuhan, China
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Wang J, Li T, Sun Q, Guo Y, Yu J, Yao Z, Hou N, Hu B. Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework. Brain Sci 2023; 13:1590. [PMID: 38002549 PMCID: PMC10670347 DOI: 10.3390/brainsci13111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD.
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Affiliation(s)
- Junyu Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Tongtong Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Qi Sun
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Yuhui Guo
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
- School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
| | - Jiandong Yu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Ning Hou
- Medical Department, The Third People’s Hospital of Tianshui, Tianshui 741000, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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Zheng G, Zheng W, Zhang Y, Wang J, Chen M, Wang Y, Cai T, Yao Z, Hu B. An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis. J Neural Eng 2023; 20:066005. [PMID: 37844568 DOI: 10.1088/1741-2552/ad038c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective.Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD.Approach.In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis.Main results.This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD.Significance.The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.
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Affiliation(s)
- Guowei Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Yu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Junyu Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Miao Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Tianhong Cai
- Institute of Computer Software and Theory, School of information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
- School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, People's Republic of China
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Zhang J, Rao VM, Tian Y, Yang Y, Acosta N, Wan Z, Lee PY, Zhang C, Kegeles LS, Small SA, Guo J. Detecting schizophrenia with 3D structural brain MRI using deep learning. Sci Rep 2023; 13:14433. [PMID: 37660217 PMCID: PMC10475022 DOI: 10.1038/s41598-023-41359-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 08/25/2023] [Indexed: 09/04/2023] Open
Abstract
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.
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Affiliation(s)
- Junhao Zhang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Vishwanatha M Rao
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Ye Tian
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Yanting Yang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Nicolas Acosta
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Zihan Wan
- Department of Applied Mathematics, Columbia University, New York, NY, USA
| | - Pin-Yu Lee
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | | | - Lawrence S Kegeles
- Department of Psychiatry, Columbia University, New York, NY, USA
- Department of Radiology, Columbia University, New York, NY, USA
| | - Scott A Small
- Department of Neurology, Radiology, and Psychiatry, Columbia University, New York, NY, USA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - Jia Guo
- Department of Psychiatry, Columbia University, New York, NY, USA.
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
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Dong J, Zhang Y, Meng Y, Yang T, Ma W, Wu H. Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks. Stem Cells Int 2022; 2022:8619690. [PMID: 36299467 PMCID: PMC9592238 DOI: 10.1155/2022/8619690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/22/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice.
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Affiliation(s)
- Jie Dong
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Yueying Zhang
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Yun Meng
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Tingxiao Yang
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Wei Ma
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Huixin Wu
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
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