{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:13:53Z","timestamp":1787915633600,"version":"build-2784847793"},"reference-count":64,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"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. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>As the incidence of neurodegenerative diseases increases, the related AI research is getting more and more advanced. In this study, we analyze the literature in this field over the last decade through bibliometric and visualization methods with the aim of mining the prominent journals, institutions, authors, and countries in this field and analyzing the keywords in order to speculate on possible future research trends.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our study extracted 1,921 relevant publications spanning 2015\u20132025 from the Web of Science Core Collection database. We conducted comprehensive bibliometric analyses and knowledge mapping visualizations using established scientometric tools: CiteSpace and Bibliometrix.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 1921 documents were included in the study, the number of publications in this field showed an overall increasing trend, and the average number of citations showed a downward trend since 2019. Among the journals, <jats:italic>Scientific Reports<\/jats:italic> had the highest number of publications. In addition, we identified 22 core journals. Institution wise, University of London has the highest participation. Among the authors, the highest number of publications is Benzinger, Tammie. The highest number of citations is Fingere Elizabeth. At the national level, the United States is number one in the world in terms of influence in this field, and China is ranked number two, both of which are well ahead of other countries and are major contributors to this field. The analysis of keywords showed the centrality of Alzheimer disease, machine learning, Parkinsons disease, and deep learning. All the studies were clustered based on keywords to get seven clusters: 0. immune infiltration; 1. Parkinsons disease; 2. multiple sclerosis; 3. mild cognitive impairment; 4. deep learning; 5. machine learning; 6. freesurfer; 7. scale. In addition, we also found the continuation of the trending topics, which are Parkinsons disease, deep learning, and machine learning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Based on the relationship between keywords and time, we speculate that there are four possible research trends: 1. Precision diagnosis with multimodal data fusion. 2. Pathological mechanism analysis and target discovery. 3. Interpretable AI and clinical translation. 4. Technology differentiation for subdivided diseases.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2025.1586282","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T05:40:59Z","timestamp":1747201259000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Knowledge map of artificial intelligence in neurodegenerative diseases: a decade-long bibliometric and visualization study"],"prefix":"10.3389","volume":"17","author":[{"given":"Junwei","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuankai","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danting","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e70225","DOI":"10.1111\/cns.70225","article-title":"Effect of different treatments on retinal thickness changes in patients with multiple sclerosis: a review","volume":"31","author":"Adibi","year":"2025","journal-title":"CNS Neurosci. 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Med. Imaging"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3390\/ph17020158","article-title":"Navigating the frontiers of machine learning in neurodegenerative disease therapeutics","volume":"17","author":"Cha","year":"2024","journal-title":"Pharmaceuticals"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: a federated transfer learning framework for wearable healthcare","volume":"35","author":"Chen","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s40708-024-00244-9","article-title":"Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines","volume":"11","author":"De Bonis","year":"2024","journal-title":"Brain Inf."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nat. Med."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"106986","DOI":"10.1016\/j.parkreldis.2024.106986","article-title":"The role of AI and machine learning in the diagnosis of Parkinson\u2019s disease and atypical Parkinsonisms","volume":"126","author":"Dennis","year":"2024","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1148\/radiol.2018180958","article-title":"A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain","volume":"290","author":"Ding","year":"2019","journal-title":"Radiology"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","article-title":"Deep learning framework for Alzheimer\u2019s disease diagnosis via 3D-CNN and FSBi-LSTM","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Access"},{"key":"ref16","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\u2019s disease","volume":"577","author":"Gate","year":"2020","journal-title":"Nature"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"e14510","DOI":"10.1002\/alz.14510","article-title":"Developing machine-learning-based amyloidogenicity predictors with cross-beta DB","volume":"21","author":"Gonay","year":"2025","journal-title":"Alzheimers Dement."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2024-0125","article-title":"Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders","author":"Goudarzi","year":"2025","journal-title":"Rev. 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Rep."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"3501","DOI":"10.21037\/qims-23-1600","article-title":"Application of convolutional neural networks in medical images: a bibliometric analysis","volume":"14","author":"Jia","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1109\/JBHI.2024.3482180","article-title":"Power of multi-modality variables in predicting Parkinson's disease progression","volume":"29","author":"Jiang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"85277","DOI":"10.1007\/s11042-024-19425-z","article-title":"Comprehensive overview of Alzheimer's disease utilizing machine learning approaches","volume":"83","author":"Kumar","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1038\/s41598-018-37769-z","article-title":"Predicting Alzheimer\u2019s disease progression using multi-modal deep learning approach","volume":"9","author":"Lee","year":"2019","journal-title":"Sci. Rep."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"807382","DOI":"10.3389\/fmed.2022.807382","article-title":"Development of a machine learning-based predictive model for lung metastasis in patients with Ewing sarcoma","volume":"9","author":"Li","year":"2022","journal-title":"Front. 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Rev. Neurol."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1038\/s41591-018-0297-y","article-title":"Blood\u2013brain barrier breakdown is an early biomarker of human cognitive dysfunction","volume":"25","author":"Nation","year":"2019","journal-title":"Nat. Med."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s40037-021-00695-4","article-title":"Bibliometrics: methods for studying academic publishing","volume":"11","author":"Ninkov","year":"2022","journal-title":"Perspect. Med. 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Technol."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"129533","DOI":"10.1016\/j.neucom.2025.129533","article-title":"A survey of artificial intelligence in gait-based neurodegenerative disease diagnosis","volume":"626","author":"Rao","year":"2025","journal-title":"Neurocomputing"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"e100751","DOI":"10.1136\/gpsych-2022-100751","article-title":"The China Alzheimer report 2022","volume":"35","author":"Ren","year":"2022","journal-title":"Gen. Psychiatry"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","article-title":"Alzheimer's disease","volume":"397","author":"Scheltens","year":"2021","journal-title":"Lancet"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1038\/s41467-019-10212-1","article-title":"Interpretable classification of Alzheimer\u2019s disease pathologies with a convolutional neural network pipeline","volume":"10","author":"Tang","year":"2019","journal-title":"Nat. Commun."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson's disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"zyaf004","DOI":"10.1093\/intbio\/zyaf004","article-title":"Identification of CXCR4 inhibitory activity in natural compounds using cheminformatics-guided machine learning algorithms","volume":"17","author":"Tripathi","year":"2025","journal-title":"Integr. Biol."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"1463078","DOI":"10.3389\/fimmu.2024.1463078","article-title":"Global research trends and hotspots in metabolomics of osteosarcoma: a decade-spanning bibliometric and visualized analysis","volume":"15","author":"Tu","year":"2024","journal-title":"Front. 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