{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T11:34:12Z","timestamp":1793014452353,"version":"4.5.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>\n                    This bibliometric review examines the evolving landscape of artificial intelligence (AI) in neurodegenerative diseases research from 2000 to March 16, 2025, utilizing data from 1,402 publications (1,159 articles, 243 reviews) indexed in the Web of Science Core Collection. Through advanced tools - VOSviewer, CiteSpace, and Bibliometrix R - the study maps collaboration networks, keyword trends, and knowledge trajectories. Results reveal exponential growth post-2017, driven by advancements in deep learning and multimodal data integration. The United States (25.96%) and China (24.11%) dominate publication volume, while the UK exhibits the highest collaboration centrality (0.24) and average citations per publication (31.68). Core journals like\n                    <jats:italic>Scientific Reports<\/jats:italic>\n                    and\n                    <jats:italic>Frontiers in Aging Neuroscience<\/jats:italic>\n                    published the most articles in this field. Highly cited publications and burst references highlight important milestones in the development history. High-frequency keywords include \u201calzheimer\u2019s disease,\u201d \u201cparkinson\u2019s disease,\u201d \u201cmagnetic resonance imaging,\u201d \u201cconvolutional neural network,\u201d \u201cbiomarkers,\u201d \u201cdementia,\u201d \u201cclassification,\u201d \u201cmild cognitive impairment,\u201d \u201cneuroimaging,\u201d and \u201cfeature extraction.\u201d Key hotspots include intelligent neuroimaging analysis, machine learning methodological iterations, molecular mechanisms and drug discovery, and clinical decision support systems for early diagnosis. Future priorities encompass advanced deep learning architectures, multi-omics integration, explainable AI systems, digital biomarker-based early detection, and transformative technologies including transformers and telemedicine. This analysis delineates AI\u2019s transformative role in optimizing diagnostics and accelerating therapeutic innovation, while advocating for enhanced interdisciplinary collaboration to bridge computational advances with clinical translation.\n                  <\/jats:p>","DOI":"10.3389\/fneur.2025.1607924","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T05:37:52Z","timestamp":1752644272000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000"],"prefix":"10.3389","volume":"16","author":[{"given":"Yabin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1038\/539179a","article-title":"Neurodegenerative Diseases","volume":"539","author":"Heemels","year":"2016","journal-title":"Nature"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s12880-024-01401-6","article-title":"Medical image analysis using improved Sam-Med2d: segmentation and classification perspectives","volume":"24","author":"Sun","year":"2024","journal-title":"BMC Med Imaging"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/molecules28186546","article-title":"A biological feature and heterogeneous network representation learning-based framework for drug-target interaction prediction","volume":"28","author":"Liu","year":"2023","journal-title":"Molecules"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10741-023-10357-8","article-title":"Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review","volume":"29","author":"Nazar","year":"2024","journal-title":"Heart Fail Rev"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10462-024-10712-7","article-title":"Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics","volume":"57","author":"Chen","year":"2024","journal-title":"Artif Intell Rev"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e3061","DOI":"10.31661\/gmj.v12i.3061","article-title":"The use of artificial intelligence in the management of neurodegenerative disorders; focus on Alzheimer's disease","volume":"12","author":"Ghasempour Dabbaghi","year":"2023","journal-title":"Galen Med J"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e0275496","DOI":"10.1371\/journal.pone.0275496","article-title":"Open banking: a bibliometric analysis-driven definition","volume":"17","author":"de Briones Araluze","year":"2022","journal-title":"PLoS One"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1510729","DOI":"10.3389\/fneur.2024.1510729","article-title":"The application of artificial intelligence in diagnosis of Alzheimer's disease: a bibliometric analysis","volume":"15","author":"An","year":"2024","journal-title":"Front Neurol"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"102285","DOI":"10.1016\/j.arr.2024.102285","article-title":"Parkinson's disease diagnosis using deep learning: a bibliometric analysis and literature review","volume":"96","author":"Abumalloh","year":"2024","journal-title":"Ageing Res Rev"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: Vosviewer, a computer program for bibliometric mapping","volume":"84","author":"van Eck","year":"2010","journal-title":"Scientometrics"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1016\/j.joi.2016.10.006","article-title":"Constructing bibliometric networks: a comparison between full and fractional counting","volume":"10","author":"Perianes-Rodriguez","year":"2016","journal-title":"J Informetr"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"5303","DOI":"10.1073\/pnas.0307513100","article-title":"Searching for intellectual turning points: progressive knowledge domain visualization","volume":"101","author":"Chen","year":"2004","journal-title":"Proc Natl Acad Sci USA"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e0223994","DOI":"10.1371\/journal.pone.0223994","article-title":"Visualizing a Field of research: a methodology of systematic Scientometric reviews","volume":"14","author":"Chen","year":"2019","journal-title":"PLoS One"},{"key":"ref14","volume-title":"How to use Citespace (C) 2015\u20132020","author":"Chen","year":"2020"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.joi.2017.08.007","article-title":"Bibliometrix: an R-tool for comprehensive science mapping analysis","volume":"11","author":"Aria","year":"2017","journal-title":"J Informetr"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/land9010028","article-title":"A bibliometric analysis on land degradation: current status, development, and future directions","volume":"9","author":"Xie","year":"2020","journal-title":"Land"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"392","DOI":"10.5195\/jmla.2022.1434","article-title":"Vosviewer and Bibliometrix","volume":"110","author":"Arruda","year":"2022","journal-title":"J Med Libr Assoc"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"16569","DOI":"10.1073\/pnas.0507655102","article-title":"An index to quantify an individual's scientific research output","volume":"102","author":"Hirsch","year":"2005","journal-title":"Proc Natl Acad Sci USA"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s11192-006-0144-7","article-title":"Theory and practise of the G-index","volume":"69","author":"Egghe","year":"2006","journal-title":"Scientometrics"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1038\/224953a0","article-title":"Bradford's law and the bibliography of science","volume":"224","author":"Brookes","year":"1969","journal-title":"Nature"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1002\/asi.22968","article-title":"Patterns of connections and movements in dual-map overlays: a new method of publication portfolio analysis","volume":"65","author":"Chen","year":"2014","journal-title":"J Am Soc Inf Sci Technol"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/S1474-4422(19)30287-X","article-title":"The genetic architecture of Parkinson's disease","volume":"19","author":"Blauwendraat","year":"2020","journal-title":"Lancet Neurol"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.neuroimage.2016.02.079","article-title":"Single subject prediction of brain disorders in neuroimaging: promises and pitfalls","volume":"145","author":"Arbabshirani","year":"2017","journal-title":"NeuroImage"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/s41586-019-1895-7","article-title":"Clonally expanded Cd8 T cells patrol the cerebrospinal fluid in Alzheimer's disease","volume":"577","author":"Gate","year":"2020","journal-title":"Nature"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1016\/j.neuroimage.2010.10.081","article-title":"Predictive markers for ad in a multi-modality framework: an analysis of mci progression in the Adni population","volume":"55","author":"Hinrichs","year":"2011","journal-title":"NeuroImage"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.tins.2017.10.001","article-title":"Predicting age using neuroimaging: innovative brain ageing biomarkers","volume":"40","author":"Cole","year":"2017","journal-title":"Trends Neurosci"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"4273","DOI":"10.1038\/s41467-018-05892-0","article-title":"Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference","volume":"9","author":"Young","year":"2018","journal-title":"Nat Commun"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","article-title":"Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural Mri","volume":"42","author":"Lian","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","article-title":"Applications of machine learning to diagnosis and treatment of neurodegenerative diseases","volume":"16","author":"Myszczynska","year":"2020","journal-title":"Nat Rev Neurol"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/S1474-4422(23)00131-X","article-title":"Neuroimaging standards for research into small vessel disease-advances since 2013","volume":"22","author":"Duering","year":"2023","journal-title":"Lancet Neurol"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.nurt.2007.05.011","article-title":"Diffusion tensor imaging of the brain","volume":"4","author":"Alexander","year":"2007","journal-title":"Neurotherapeutics"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3389\/fneur.2019.00237","article-title":"Editorial: computerized magnetic resonance imaging-based neuroimaging of neurodegenerative diseases","volume":"10","author":"M\u00fcller","year":"2019","journal-title":"Front Neurol"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1423515","DOI":"10.3389\/fnagi.2024.1423515","article-title":"Deep learning-based quantification of brain atrophy using 2d T1-weighted Mri for Alzheimer's disease classification","volume":"16","author":"Park","year":"2024","journal-title":"Front Aging Neurosci"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"1456069","DOI":"10.3389\/frai.2024.1456069","article-title":"Advanced interpretable diagnosis of Alzheimer's disease using Secnn-Rf framework with explainable Ai","volume":"7","author":"AbdelAziz","year":"2024","journal-title":"Front Artif Intell"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0538-23.2024","article-title":"Fixel-based analysis reveals tau-related white matter changes in early stages of Alzheimer's disease","volume":"44","author":"Ahmadi","year":"2024","journal-title":"J Neurosci"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1373502","DOI":"10.3389\/fninf.2024.1373502","article-title":"Brain Mri sequence and view plane identification using deep learning","volume":"18","author":"Ali","year":"2024","journal-title":"Front Neuroinform"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"1399931","DOI":"10.3389\/fninf.2024.1399931","article-title":"Artificial intelligence role in advancement of human brain connectome studies","volume":"18","author":"Shekouh","year":"2024","journal-title":"Front Neuroinform"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"1640","DOI":"10.1002\/hbm.25748","article-title":"Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction","volume":"43","author":"Ren","year":"2022","journal-title":"Hum Brain Mapp"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"535","DOI":"10.3762\/bjnano.15.47","article-title":"On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems","volume":"15","author":"He","year":"2024","journal-title":"Beilstein J Nanotechnol"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.030503","article-title":"Earlier detection of Alzheimer\u2019s disease using 3D-convolutional neural networks","volume":"46","author":"Nithya","year":"2023","journal-title":"Comput Syst Sci Eng"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13020260","article-title":"Ovitad: optimized vision transformer to predict various stages of Alzheimer's disease using resting-state fmri and structural mri data","volume":"13","author":"Sarraf","year":"2023","journal-title":"Brain Sci"},{"key":"ref42","first-page":"5","author":"Sherwani","year":"2023"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/e22121340","article-title":"Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers","volume":"22","author":"Nam Nguyen","year":"2020","journal-title":"Entropy (Basel)"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"9","DOI":"10.24138\/jcomss-2022-0157","article-title":"Machine learning algorithms for classification patients with Parkinson\u2019S disease and hereditary ataxias","volume":"19","author":"Escamilla-Luna","year":"2023","journal-title":"J Commun Softw Syst"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s11571-022-09787-1","article-title":"Neurodegenerative diseases-caps: a capsule network based early screening system for the classification of neurodegenerative diseases","volume":"16","author":"Bhatele","year":"2022","journal-title":"Cogn Neurodyn"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3390\/app14072741","article-title":"Automatic medical image segmentation with vision transformer","volume":"14","author":"Zhang","year":"2024","journal-title":"Appl Sci"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00441-023-03816-z","article-title":"The benefits and pitfalls of machine learning for biomarker discovery","volume":"394","author":"Ng","year":"2023","journal-title":"Cell Tissue Res"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.3390\/biom11060897","article-title":"Current approaches and tools used in drug development against Parkinson's disease","volume":"11","author":"Kosz\u0142a","year":"2021","journal-title":"Biomol Ther"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3390\/ph16101459","article-title":"Identification of potential Jnk3 inhibitors: a combined approach using molecular docking and deep learning-based virtual screening","volume":"16","author":"Yao","year":"2023","journal-title":"Pharmaceuticals (Basel)"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1016\/j.jpha.2023.06.011","article-title":"Integrative multi-omics and systems bioinformatics in translational neuroscience: a data mining perspective","volume":"13","author":"O'Connor","year":"2023","journal-title":"J Pharm Anal"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"1414886","DOI":"10.3389\/fnmol.2024.1414886","article-title":"Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs","volume":"17","author":"Carraro","year":"2024","journal-title":"Front Mol Neurosci"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1285905","DOI":"10.3389\/fnagi.2024.1285905","article-title":"Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease","volume":"16","author":"Yang","year":"2024","journal-title":"Front Aging Neurosci"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"612","DOI":"10.25122\/jml-2020-0182","article-title":"Development of a knowledge-based clinical decision support system for multiple sclerosis diagnosis","volume":"13","author":"Hosseini","year":"2020","journal-title":"J Med Life"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s12021-017-9324-2","article-title":"Multi-view ensemble classification of brain connectivity images for neurodegeneration type discrimination","volume":"15","author":"Fratello","year":"2017","journal-title":"Neuroinformatics"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s12911-020-1027-1","article-title":"Designing a Mhealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach","volume":"20","author":"Timotijevic","year":"2020","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s13755-023-00231-0","article-title":"Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review","volume":"11","author":"Huang","year":"2023","journal-title":"Health Inf Sci Syst"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2025.1607924\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T15:46:33Z","timestamp":1753285593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2025.1607924\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,16]]},"references-count":56,"alternative-id":["10.3389\/fneur.2025.1607924"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2025.1607924","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,16]]},"article-number":"1607924"}}