[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119967-en":3,"doc-seo-119967-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119967,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Novel Approach to Dementia Prediction of DTI Markers Using BALI, LIBRA and Machine Learning Techniques","Early prediction of dementia and monitoring disease progression remain challenging, particularly because DTI acquisition is expensive and time-consuming. A novel machine learning framework integrates multimodal neuroimaging biomarkers with inexpensive, readily available clinical factors to estimate fractional anisotropy and dementia severity. Recursive Feature Elimination selects the most effective predictors from BALI and LIBRA features, and an interpretable decision tree is trained for prediction. The resulting model reaches 96.25% accuracy on an independent test set, enabling objective dementia screening without requiring new DTI scans.","A Novel Approach to Dementia Prediction of DTI Markers Using BALI, LIBRA, and Machine Learning Techniques  \nAhmad Akbarifar1, Adel Maghsoudpour1, Fatemeh Mohammadian2, Morteza Mohammadzaheri3, Omid Ghaemi4  \n1Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2Department of Psychiatry, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran 3College of Engineering, Birmingham City University, Birmingham, UK  \n4Department of Radiology and Interventional Radiology, Imam Khomeini Hospital (Imaging Centre) and Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran  \nA. A. is a PhD candidate at the Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran. Iran (e-mail: [ahmad.akbarifar@srbiau.ac.ir](ahmad.akbarifar@srbiau.ac.ir)).  \n[A. M. is](A. M. is) an Assistant Professor of the Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran. Iran (corresponding author to provide phone: 021-44865154-8; fax: 021-44865166; Postal Code 1477893855Tehran, Iran; e-mail: [a.maghsoudpour@srbiau.ac.ir](a.maghsoudpour@srbiau.ac.ir)).  \n[F. M. is](F. M. is) an Assistant Professor at the Department of Psychiatry, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran (email: [fmohammadianr@sina.tums.ac.ir](fmohammadianr@sina.tums.ac.ir)).  \n[M. M. is](M. M. is) a Senior Lecturer at the College of Engineering, Birmingham City University, Birmingham, B4 7XG, UK (e-mail: [morteza.mohammadzaheri@bcu.ac.uk](morteza.mohammadzaheri@bcu.ac.uk)).  \n[O. G. is](O. G. is) an Assistant Professor at the Department of Radiology and Interventional Radiology, Imam Khomeini Hospital (Imaging Centre) and Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran (e-mail: [oghaemi@sina.tums.ac.ir](oghaemi@sina.tums.ac.ir)).  \nConflicts of Interest  \nAll authors declare that they have no conflicts of interest to disclose.  \nConsent Statement  \nAll authors confirm that all human subjects, involved in the reported research, provided informed consent.  \nCompeting interests  \nThe authors declare no competing interests.  \nAcknowledgment  \nThe authors wish to especially thank at Advanced Diagnostic and Interventional Radiology Research Centre (ADIR), Imam Khomeini Complex Hospital، Tehran University of Medical Science, Tehran, Iran, for their kind support of this project.  \nAbstract  \nEarly prediction of dementia and disease progression remains challenging. This study presents a novel machine learning framework for dementia diagnosis by integrating multimodal neuroimaging biomarkers and inexpensive and readily available clinical factors. Fractional anisotropy (FA) measurements in diffusion tensor imaging (DTI) provide microstructural insight into white matter integrity disturbances in dementia. However, the acquisition of DTI is costly and time-consuming. We applied Recursive Feature Elimination (RFE) to identify predictors from structural measures of the 9 factors of Brain Atrophy and Lesion Index (BALI) factors and 42 factors of Clinical Lifestyle for Brain Health (LIBRA) factors to estimate FA in DTI. The 10 most effective features of BALI/ LIBRA selected by RFE were used to train an interpretable decision tree model to predict the severity of dementia from DTI. A decision tree model based on biomarkers selected by RFE achieved an accuracy of 96.25% in predicting dementia in an independent test set. This integrated framework pioneers the prediction of white matter microstructural changes from available structural/ clinical factors using machine learning. By avoiding DTI acquisition, our approach provides a practical and objective tool to improve dementia screening and progress monitoring. The Identification of key predictive markers of BALI/ LIBRA will also provide information on the mechanisms of lifestyle-related disease mechanisms, neurodegeneration, and white matter dysfunct","cbCaihvum18y1cDM","https://ap.wps.com/l/cbCaihvum18y1cDM","pdf",392112,1,17,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is early dementia prediction difficult in current practice?\",\"answer\":\"Diagnosis is challenging in early stages because symptoms can be subtle, and existing approaches rely heavily on neuropsychological assessments and structural imaging without sufficiently quantitative biomarkers.\"},{\"question\":\"What data sources does the proposed framework use to predict DTI-related measures?\",\"answer\":\"It integrates multimodal neuroimaging biomarkers (BALI-derived structural measures) with clinical factors (LIBRA factors) and uses them to estimate fractional anisotropy severity related to DTI.\"},{\"question\":\"How are the most useful predictors selected and how is the prediction made?\",\"answer\":\"Recursive Feature Elimination selects the 10 most effective BALI/LIBRA features, and these selected biomarkers are used to train an interpretable decision tree model for dementia severity prediction.\"}]","A Novel Approach to Dementia Prediction of DTI Markers Using BALI, LIBRA and Machine Learning Techniques | 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is early dementia prediction difficult in current practice?","Question",{"text":75,"@type":76},"Diagnosis is challenging in early stages because symptoms can be subtle, and existing approaches rely heavily on neuropsychological assessments and structural imaging without sufficiently quantitative biomarkers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources does the proposed framework use to predict DTI-related measures?",{"text":80,"@type":76},"It integrates multimodal neuroimaging biomarkers (BALI-derived structural measures) with clinical factors (LIBRA factors) and uses them to estimate fractional anisotropy severity related to DTI.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the most useful predictors selected and how is the prediction made?",{"text":84,"@type":76},"Recursive Feature Elimination selects the 10 most effective BALI/LIBRA features, and these selected biomarkers are used to train an interpretable decision tree model for dementia severity 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