Diagnosis of Early Mild Cognitive Impairment in Type 2 Diabetes Mellitus by Deep Learning of Multimodal Images and Metadata
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, which can result in abnormal brain alternations and mild cognitive impairment in elder adult lives. Therefore, identification of early cognitive impairment in patients with T2DM is of paramount importance for mitigating cognitive decli...
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Published in | 2024 IEEE International Symposium on Biomedical Imaging (ISBI) pp. 1 - 4 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
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IEEE
27.05.2024
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Abstract | Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, which can result in abnormal brain alternations and mild cognitive impairment in elder adult lives. Therefore, identification of early cognitive impairment in patients with T2DM is of paramount importance for mitigating cognitive decline of patients and enhancing their quality of life. Moreover, clinical metadata is informative in T2DM diagnosis, which can provide prior knowledge in demonstrating different severities of T2DM. To this end, we develop a robust deep learning model for diagnosing early cognitive impairment in T2DM using multi-modal neuroimages, which incorporates the informative clinical metadata (i.e., MoCA, BMI and HbA1c) to design metadata-induced contrastive Laplacian regularization. This can effectively alleviate the problem of small medical dataset in deep learning. Extensive experiments have shown significant improvement in accuracy and AUC in the identification of T2DM with/without mild cognitive impairment in a dataset with 311 subjects, indicating the ability of the proposed method in understanding of associated brain alterations in T2DM and its potential applications on other brain disorders. |
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AbstractList | Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, which can result in abnormal brain alternations and mild cognitive impairment in elder adult lives. Therefore, identification of early cognitive impairment in patients with T2DM is of paramount importance for mitigating cognitive decline of patients and enhancing their quality of life. Moreover, clinical metadata is informative in T2DM diagnosis, which can provide prior knowledge in demonstrating different severities of T2DM. To this end, we develop a robust deep learning model for diagnosing early cognitive impairment in T2DM using multi-modal neuroimages, which incorporates the informative clinical metadata (i.e., MoCA, BMI and HbA1c) to design metadata-induced contrastive Laplacian regularization. This can effectively alleviate the problem of small medical dataset in deep learning. Extensive experiments have shown significant improvement in accuracy and AUC in the identification of T2DM with/without mild cognitive impairment in a dataset with 311 subjects, indicating the ability of the proposed method in understanding of associated brain alterations in T2DM and its potential applications on other brain disorders. |
Author | Yue, Xiaomei Li, Gang Han, Kangfu Qiu, Shijun Yang, Feng |
Author_xml | – sequence: 1 givenname: Kangfu surname: Han fullname: Han, Kangfu organization: Southern Medical University,School of Biomedical Engineering,Guangzhou,China – sequence: 2 givenname: Xiaomei surname: Yue fullname: Yue, Xiaomei organization: University of North Carolina,Department of Radiology and BRIC,Chapel Hill,NC,USA – sequence: 3 givenname: Shijun surname: Qiu fullname: Qiu, Shijun organization: Guangzhou University of Chinese Medicine,The First Affiliated Hospital,Department of Radiology,Guangzhou,China – sequence: 4 givenname: Feng surname: Yang fullname: Yang, Feng organization: Southern Medical University,School of Biomedical Engineering,Guangzhou,China – sequence: 5 givenname: Gang surname: Li fullname: Li, Gang organization: University of North Carolina,Department of Radiology and BRIC,Chapel Hill,NC,USA |
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Snippet | Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, which can result in abnormal brain alternations and mild cognitive impairment in elder adult... |
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SubjectTerms | Accuracy Contrastive Laplacian Regularization Deep learning Diabetes Laplace equations Medical diagnostic imaging Meta-data Metadata Multi-modal Neuroimages Task analysis Type 2 Diabetes Mellitus |
Title | Diagnosis of Early Mild Cognitive Impairment in Type 2 Diabetes Mellitus by Deep Learning of Multimodal Images and Metadata |
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