MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus
Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients’ cognitive function and early intervention is helpful to improve patient’s quality of life. At present, neuropsychiatric screening tests is often used to perform this task i...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2022.1082794/full |
| _version_ | 1811179669076049920 |
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| author | Zhigao Xu Lili Zhao Lei Yin Yan Liu Ying Ren Guoqiang Yang Guoqiang Yang Jinlong Wu Feng Gu Xuesong Sun Hui Yang Taisong Peng Jinfeng Hu Xiaogeng Wang Minghao Pang Qiong Dai Guojiang Zhang |
| author_facet | Zhigao Xu Lili Zhao Lei Yin Yan Liu Ying Ren Guoqiang Yang Guoqiang Yang Jinlong Wu Feng Gu Xuesong Sun Hui Yang Taisong Peng Jinfeng Hu Xiaogeng Wang Minghao Pang Qiong Dai Guojiang Zhang |
| author_sort | Zhigao Xu |
| collection | DOAJ |
| description | Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients’ cognitive function and early intervention is helpful to improve patient’s quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer’s disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM.Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM).Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index.Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers.Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance. |
| first_indexed | 2024-04-11T06:37:24Z |
| format | Article |
| id | doaj.art-c4960b75140f49fabb49f10b1a5d1c2e |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-11T06:37:24Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-c4960b75140f49fabb49f10b1a5d1c2e2022-12-22T04:39:38ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852022-11-011010.3389/fbioe.2022.10827941082794MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitusZhigao Xu0Lili Zhao1Lei Yin2Yan Liu3Ying Ren4Guoqiang Yang5Guoqiang Yang6Jinlong Wu7Feng Gu8Xuesong Sun9Hui Yang10Taisong Peng11Jinfeng Hu12Xiaogeng Wang13Minghao Pang14Qiong Dai15Guojiang Zhang16Department of Radiology, Radiology-Based AI Innovation Workroom, The Third People’s Hospital of Datong, Datong, ChinaDepartment of Radiology, Radiology-Based AI Innovation Workroom, The Third People’s Hospital of Datong, Datong, ChinaGraduate School, Changzhi Medical College, Changzhi, ChinaDepartment of Endocrinology, The Third People’s Hospital of Datong, Datong, ChinaDepartment of Materials Science and Engineering, Henan University of Technology, Zhengzhou, ChinaCollege of Medical Imaging, Shanxi Medical University, Taiyuan, ChinaDepartment of Radiology, First Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Radiology, Radiology-Based AI Innovation Workroom, The Third People’s Hospital of Datong, Datong, ChinaDepartment of Radiology, Radiology-Based AI Innovation Workroom, The Third People’s Hospital of Datong, Datong, ChinaMedical Department, The Third People’s Hospital of Datong, Datong, ChinaDepartment of Radiology, Radiology-Based AI Innovation Workroom, The Third People’s Hospital of Datong, Datong, ChinaDepartment of Radiology, The Second People’s Hospital of Datong, Datong, ChinaDepartment of Radiology, The Second People’s Hospital of Datong, Datong, ChinaDepartment of Radiology, Affiliated Hospital of Datong University, Datong, China0Department of Radiology, The People’s Hospital of Yunzhou District, Datong, China1Huiying Medical Technology (Beijing) Co. Ltd, Beijing, China2Department of Cardiovasology, Department of Science and Education, The Third People’s Hospital of Datong, Datong, ChinaBackground: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients’ cognitive function and early intervention is helpful to improve patient’s quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer’s disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM.Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM).Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index.Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers.Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.https://www.frontiersin.org/articles/10.3389/fbioe.2022.1082794/fullMRImachine learning modelmild cognitive impairmentdementiatype 2 diabetes mellitus |
| spellingShingle | Zhigao Xu Lili Zhao Lei Yin Yan Liu Ying Ren Guoqiang Yang Guoqiang Yang Jinlong Wu Feng Gu Xuesong Sun Hui Yang Taisong Peng Jinfeng Hu Xiaogeng Wang Minghao Pang Qiong Dai Guojiang Zhang MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus Frontiers in Bioengineering and Biotechnology MRI machine learning model mild cognitive impairment dementia type 2 diabetes mellitus |
| title | MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus |
| title_full | MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus |
| title_fullStr | MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus |
| title_full_unstemmed | MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus |
| title_short | MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus |
| title_sort | mri based machine learning model a potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus |
| topic | MRI machine learning model mild cognitive impairment dementia type 2 diabetes mellitus |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2022.1082794/full |
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