[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126059-en":3,"doc-seo-126059-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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},126059,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Curvature estimation techniques for advancing neurodegenerative disease analysis - a systematic review of machine learning and deep learning approaches","Neurodegenerative diseases require advanced analytic methods to characterize complex brain structures and their temporal changes. Curvature estimation has become an important tool in neuroimaging and pattern recognition because it captures geometric structure and shape variation within data. This systematic review evaluates modern curvature estimation approaches spanning classical mathematics, machine learning, deep learning, and hybrid methods. Analysis of 105 papers from 2010–2023 examines how these methods improve understanding of neurodegenerative pathology’s structural variations and supports development of better diagnostic tools and interventions.","Am J Neurodegener Dis 2025;14(1):1-33  \n[www.AJND.us](www.AJND.us /ISSN:2165-591X/AJND0161674)[ /ISSN:2165-591X/AJND0161674](www.AJND.us /ISSN:2165-591X/AJND0161674)  \nReview Article  \nCurvature estimation techniques for advancing neurodegenerative disease analysis: a systematic review of machine learning and deep learning approaches  \nSeyed-Ali Sadegh-Zadeh1, Nasrin Sadeghzadeh2, Bahareh Sedighi3, Elaheh Rahpeyma4, Mahdiyeh Nilgounbakht5, Mohammad Amin Barati6  \n1Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom; 2Faculty of Mathematics, University of Qom, Qom, Iran; 3Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran; 4Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran; 5Department of Computer Engineering, Tabriz University, Tabriz, Iran;  \n6School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran  \nReceived November 2, 2024; Accepted February 7, 2025; Epub February 15, 2025; Published February 28, 2025  \nAbstract: Neurodegenerative diseases present complex challenges that demand advanced analytical techniques to decode intricate brain structures and their changes over time. Curvature estimation within datasets has emerged as a critical tool in areas like neuroimaging and pattern recognition, with significant applications in diagnosing and understanding neurodegenerative diseases. This systematic review assesses state-of-the-art curvature estimation methodologies, covering classical mathematical techniques, machine learning, deep learning, and hybrid methods. Analysing 105 research papers from 2010 to 2023, we explore how each approach enhances our understanding of structural variations in neurodegenerative pathology. Our findings highlight a shift from classical methods to machine learning and deep learning, with neural network regression and convolutional neural networks gaining traction due to their precision in handling complex geometries and data-driven modelling. Hybrid methods further demonstrate the potential to merge classical and modern techniques for robust curvature estimation. This comprehensive review aims to equip researchers and clinicians with insights into effective curvature estimation methods, supporting the development of enhanced diagnostic tools and interventions for neurodegenerative diseases.  \nKeywords: Curvature estimation, dataset analysis, machine learning methods, deep learning techniques, systematic review  \nIntroduction  \nThe process of estimating the curvature of adataset is an essential task in many applications, including computer graphics, computer vision, and pattern recognition [1] . Curvature, in this context, refers to the rate of change of the orientation of a curve or surface at a given point. Estimating the curvature of a dataset can provide valuable information about its shape, contour, and geometric properties, which can be used to perform various analysis and processing tasks [2] .  \nDataset curvature encapsulates the geometric structure and shape variations within the data. In clinical settings, particularly in neurodegenerative disease analysis, these variations can  \nhighlight morphological changes in critical brain regions. For example, local curvature changes in the hippocampus and cortical regions are pivotal for early diagnosis of conditions like Alzheimer’s disease [3-5] . Understanding these curvatures enables clinicians to map disease progression and develop precise therapeutic strategies, aligning with personalized medicine’s goals [6] .  \nIn recent years, the field of computer graphics, computer vision, and pattern recognition has seen significant advancements in the development of algorithms and methods for estimating the curvature of datasets [7] . However, due to the diverse nature of datasets and the different requirements of various applications, the literature on curvature","cbCaibEuF94WhD6q","https://ap.wps.com/l/cbCaibEuF94WhD6q","pdf",1332261,1,33,"English","en",105,"# Introduction\n## Dataset curvature in clinical neurodegenerative analysis\n# Curvature estimation for neurodegenerative analysis\n## Research objective and key question\n## Evolution from classical to ML and deep learning\n# Clinical implications of dataset curvature","[{\"question\":\"What does curvature estimation mean in the context of dataset analysis?\",\"answer\":\"Curvature refers to how the orientation of a curve or surface changes at a point, and estimating it reveals shape, contour, and geometric properties that support analysis and processing tasks.\"},{\"question\":\"Which kinds of methods are reviewed for curvature estimation?\",\"answer\":\"The review covers classical mathematical techniques, machine learning approaches, deep learning approaches, and hybrid methods that combine classical and modern ideas.\"},{\"question\":\"What trend does the review identify across the literature from 2010 to 2023?\",\"answer\":\"Findings indicate a shift from classical methods toward machine learning and deep learning, with neural network regression and convolutional neural networks becoming more prominent for modeling complex geometries.\"}]","Curvature estimation techniques for advancing neurodegenerative disease analysis - a systematic review of machine learning and deep learning approaches | PDF",1785902836,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"curvature-estimation-techniques-for-advancing-neurodegenerative-disease-analysis-a-systematic-review-of-machine-learning-and-deep-learning-approaches","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/curvature-estimation-techniques-for-advancing-neurodegenerative-disease-analysis-a-systematic-review-of-machine-learning-and-deep-learning-approaches/126059/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does curvature estimation mean in the context of dataset analysis?","Question",{"text":76,"@type":77},"Curvature refers to how the orientation of a curve or surface changes at a point, and estimating it reveals shape, contour, and geometric properties that support analysis and processing tasks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which kinds of methods are reviewed for curvature estimation?",{"text":81,"@type":77},"The review covers classical mathematical techniques, machine learning approaches, deep learning approaches, and hybrid methods that combine classical and modern ideas.",{"name":83,"@type":74,"acceptedAnswer":84},"What trend does the review identify across the literature from 2010 to 2023?",{"text":85,"@type":77},"Findings indicate a shift from classical methods toward machine learning and deep learning, with neural network regression and convolutional neural networks becoming more prominent for modeling complex geometries.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]