{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T11:15:27Z","timestamp":1783422927550,"version":"3.54.6"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Neurodegenerative diseases such as Parkinson\u2019s disease (PD) and Alzheimer\u2019s disease (AD) present a significant and growing challenge to the healthcare systems worldwide. Both conditions are progressive and often undetected early, making timely diagnosis crucial. Recently, breakthroughs in computer vision and artificial intelligence have enabled the development of non-invasive and cost-effective screening and decision-making tools, allowing for earlier detection of the disease. This review serves as a comprehensive guide, providing structured insights into computational research methods for automated detection of PD and AD, with focus on handwriting analysis as a subtle behavioral biomarker of neurological impairment. A range of methodologies is examined, including static and dynamic handwriting assessment, feature engineering procedures, deep learning and classical ML-based approaches. The analysis emphasizes the most effective methods, the handwriting features found to be most revealing, the datasets most used in the literature, and the performance levels reported for each disease. Several studies report that transfer learning based on convolutional neural networks and transformer-based architectures for Parkinson\u2019s disease diagnosis is regularly able to achieve high accuracy, frequently above 95% on benchmark datasets. In contrast, Alzheimer\u2019s disease research is progressively benefitting from multimodal approaches combining kinematic and spatial handwriting features to capture cognitive and motor changes. Structured summaries of publicly available handwriting datasets are provided, and critical advancements, ongoing challenges, and future research priorities are discussed. The integration of insights across the studies, through this work, aims to assist researchers and clinicians in the development and translation of handwriting-based, AI-guided diagnostic tools for neurodegenerative diseases.<\/jats:p>","DOI":"10.3390\/s26134190","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:48:32Z","timestamp":1782992912000},"page":"4190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Handwriting as a Biomarker for Early Detection of Parkinson\u2019s and Alzheimer\u2019s Diseases: A Comprehensive Guide for Researchers"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6137-4642","authenticated-orcid":false,"given":"Ameur","family":"Bensefia","sequence":"first","affiliation":[{"name":"Computer & Information Sciences Division, Higher Colleges of Technology, Abu Dhabi 41012, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8941-8440","authenticated-orcid":false,"given":"Nafeth","family":"Al Hashlamoun","sequence":"additional","affiliation":[{"name":"Computer & Information Sciences Division, Higher Colleges of Technology, Abu Dhabi 41012, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"a028035","DOI":"10.1101\/cshperspect.a028035","article-title":"Pathology of Neurodegenerative Diseases","volume":"9","author":"Dugger","year":"2017","journal-title":"Cold Spring Harb. Perspect. Biol."},{"key":"ref_2","unstructured":"Greenblat, C. (2025, December 12). Dementia. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_4","unstructured":"Gauthier, S., Rosa-Neto, P., Morais, J.A., and Webster, C. (2021). World Alzheimer Report 2021: Journey Through the Diagnosis of Dementia, Alzheimer\u2019s Disease International."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Villase\u00f1or, L., Ochoa-Ruiz, G., Montes Rivera, M., Barr\u00f3n-Estrada, M.L., and Acosta-Mesa, H.G. (2025). Exploring Deep Learning Applications in Neurodegenerative Diseases: A State-of-the-Art Review. Advances in Computational Intelligence, Springer Nature. MICAI 2024 International Workshops.","DOI":"10.1007\/978-3-031-83879-8"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s10462-024-10855-7","article-title":"Multistage transfer learning for medical images","volume":"57","author":"Ayana","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_7","unstructured":"Bensefia, A., Paquet, T., and Heutte, L. (2003, January 2\u20135). Grapheme based writer verification. Proceedings of the 11th Conference of the International Graphonomics Society, Scottsdale, AZ, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.47857\/irjms.2025.v06i04.06552","article-title":"The Multimodal Fusion of Voice, Gait, and Handwriting Detection of Parkinson\u2019s Disease Using Machine Learning","volume":"6","author":"Meghashree","year":"2025","journal-title":"Int. Res. J. Multidiscip. Scope"},{"key":"ref_9","first-page":"25424823251385510","article-title":"Hand motor function of patients with early- and late-onset Alzheimer\u2019s disease and its relation to activities of daily living","volume":"9","author":"Stetter","year":"2025","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1159\/000068484","article-title":"Kinematic analysis of handwriting movements in patients with Alzheimer\u2019s disease, mild cognitive impairment, depression and healthy subjects","volume":"15","author":"Mergl","year":"2003","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/nrn1007","article-title":"Unfolding the role of protein misfolding in neurodegenerative diseases","volume":"4","author":"Soto","year":"2003","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson\u2019s disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/nrn.2017.62","article-title":"Non-motor features of Parkinson disease","volume":"18","author":"Schapira","year":"2017","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5143","DOI":"10.1002\/alz.13859","article-title":"Revised criteria for diagnosis and staging of Alzheimer\u2019s disease: Alzheimer\u2019s Association Workgroup","volume":"20","author":"Jack","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1093\/procel\/pwae026","article-title":"Alzheimer\u2019s disease: Insights into pathology, molecular mechanisms, and therapy","volume":"16","author":"Zheng","year":"2025","journal-title":"Protein Cell"},{"key":"ref_16","unstructured":"Alzheimer Association (2025). American Perspectives on Early Detection of Alzheimer\u2019s Disease in the Era of Treatment, Alzheimer Association."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, J., Cho, E., Lee, H., Lee, S., Kim, S., and Kim, J. (2025). Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers. Biosensors, 15.","DOI":"10.3390\/bios15020102"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","article-title":"Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis","volume":"86","author":"Rizzo","year":"2016","journal-title":"Neurology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"476","DOI":"10.2967\/jnumed.113.120618","article-title":"Appropriate Use Criteria for Amyloid PET: A Report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer\u2019s Association","volume":"54","author":"Johnson","year":"2013","journal-title":"J. Nucl. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson\u2019s disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Van der Weel, F.R., and Van der Meer, A.L.H. (2024). Handwriting but not typewriting leads to widespread brain connectivity: A high-density EEG study with implications for the classroom. Front. Psychol., 14.","DOI":"10.3389\/fpsyg.2023.1219945"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Arraziqi, D., Sardjono, T.A., Miawarni, H., and Purnomo, M.H. (2020, January 17\u201318). Detection of Parkinson\u2019s Disease at The Level of Motor Experiences of Daily Living Using Spiral Handwriting. Proceedings of the 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), Surabaya, Indonesia.","DOI":"10.1109\/CENIM51130.2020.9297932"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.cmpb.2016.08.005","article-title":"A new computer vision-based approach to aid the diagnosis of Parkinson\u2019s disease","volume":"136","author":"Pereira","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lu, H., Qi, G., Wu, D., Lin, C., Ma, S., Shi, Y., and Xue, H. (2025). A novel feature extraction method based on dynamic handwriting for Parkinson\u2019s disease detection. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0318021"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Botto-Tobar, M., Zambrano Vizuete, M., Montes Le\u00f3n, S., Torres-Carri\u00f3n, P., and Durakovic, B. (2024). A Fine-Tuned Transfer Learning Approach for Parkinson\u2019s Disease Detection on New Hand PD Dataset. International Conference on Applied Technologies, Springer Nature.","DOI":"10.1007\/978-3-031-58953-9"},{"key":"ref_26","first-page":"871","article-title":"Image-based Parkinson disease detection using deep transfer learning and optimization algorithm","volume":"16","author":"Agrawal","year":"2024","journal-title":"Int. J. Inf. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2686","DOI":"10.1049\/ipr2.12820","article-title":"A coordinate attention enhanced swin transformer for handwriting recognition of Parkinson\u2019s disease","volume":"17","author":"Wang","year":"2023","journal-title":"IET Image Process"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.future.2020.11.020","article-title":"Handwriting dynamics assessment using deep neural network for early identification of Parkinson\u2019s disease","volume":"117","author":"Kamran","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"135600","DOI":"10.1109\/ACCESS.2023.3337804","article-title":"Multi-Model Fusion of CNNs for Identification of Parkinson\u2019s Disease Using Handwritten Samples","volume":"11","author":"Naz","year":"2023","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bensefia, A., Zarrad, A., and Hashalamon, N.A. (2025, January 6). Vision Transformer-Based Classification of Parkinson\u2019s Disease Using Hand-Drawn Spiral Patterns. Proceedings of the 2025 10th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates.","DOI":"10.1109\/ITT69610.2025.11352923"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103551","DOI":"10.1016\/j.bspc.2022.103551","article-title":"Tremor-related feature engineering for machine learning based Parkinson\u2019s disease diagnostics","volume":"75","author":"Valla","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s40860-021-00130-9","article-title":"A systematic approach to diagnose Parkinson\u2019s disease through kinematic features extracted from handwritten drawings","volume":"7","author":"Lamba","year":"2021","journal-title":"J. Reliab. Intell. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104283","DOI":"10.1016\/j.ijmedinf.2020.104283","article-title":"A novel ensemble of random forest for assisting diagnosis of Parkinson\u2019s disease on small handwritten dynamics dataset","volume":"144","author":"Xu","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TNSRE.2014.2359997","article-title":"Decision Support Framework for Parkinson\u2019s Disease Based on Novel Handwriting Markers","volume":"23","author":"Mekyska","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"114405","DOI":"10.1016\/j.eswa.2020.114405","article-title":"Sequence-based dynamic handwriting analysis for Parkinson\u2019s disease detection with one-dimensional convolutions and BiGRUs","volume":"168","author":"Diaz","year":"2021","journal-title":"Expert. Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.patrec.2019.08.018","article-title":"Dynamically enhanced static handwriting representation for Parkinson\u2019s disease detection","volume":"128","author":"Diaz","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pereira, C.R., Weber, S.A.T., Hook, C., Rosa, G.H., and Papa, J.P. (2016, January 4\u20137). Deep Learning-Aided Parkinson\u2019s Disease Diagnosis from Handwritten Dynamics. Proceedings of the 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), S\u00e3o Jos\u00e9 dos Campos, Brazil.","DOI":"10.1109\/SIBGRAPI.2016.054"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.artmed.2016.01.004","article-title":"Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson\u2019s disease","volume":"67","author":"Mekyska","year":"2016","journal-title":"Artif. Intell. Med."},{"key":"ref_39","unstructured":"Isenkul, M., Sakar, B., and Kursun, O. (2014, January 22\u201324). Improved Spiral Test Using Digitized Graphics Tablet for Monitoring Parkinson\u2019s Disease. Proceedings of the 2nd International Conference on e-Health and Telemedicine, Istanbul, Turkey."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104822","DOI":"10.1016\/j.engappai.2022.104822","article-title":"Diagnosing Alzheimer\u2019s disease from on-line handwriting: A novel dataset and performance benchmarking","volume":"111","author":"Cilia","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Saha, R., Mukherjee, A., Bal, A., Chakrabarti, A., and Mandal, S.N. (2025). A Novel Non-invasive Handwriting-Based Machine Learning Approach for Early Detection of Alzheimer\u2019s Disease. Arab. J. Sci. Eng., 1\u201332.","DOI":"10.1007\/s13369-025-10702-z"},{"key":"ref_42","unstructured":"Vento, M., and Percannella, G. (2019). Handwriting Analysis to Support Alzheimer\u2019s Disease Diagnosis: A Preliminary Study. Computer Analysis of Images and Patterns, Springer International Publishing."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e70006","DOI":"10.1049\/htl2.70006","article-title":"Writing the Signs: An Explainable Machine Learning Approach for Alzheimer\u2019s Disease Classification from Handwriting","volume":"12","author":"Ho","year":"2025","journal-title":"Healthc. Technol. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rehman, S.U., and Mitra, U. (2025). Cognitive Handwriting Insights for Alzheimer\u2019s Diagnosis: A Hybrid Framework. Information, 16.","DOI":"10.3390\/info16030249"},{"key":"ref_45","unstructured":"Azzali, I., Cilia, N.D., De Stefano, C., Fontanella, F., Giacobini, M., and Vanneschi, L. (2022). Vectorial GP for Alzheimer\u2019s Disease Prediction Through Handwriting Analysis. Applications of Evolutionary Computation, Springer. Available online: https:\/\/api.semanticscholar.org\/CorpusID:248301888."},{"key":"ref_46","first-page":"105003","article-title":"Time\u2013Frequency representation of online handwriting for neurodegenerative disease classification","volume":"91","author":"Duda","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lozupone, G., Nardone, E., Pace, C.D., and D\u2019Alessandro, T. (2024). Transformers and CNNs in Neurodiagnostics: Handwriting Analysis for Alzheimer\u2019s Diagnosis. Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, 1\u20135 December 2024, Proceedings, Part XI, Springer.","DOI":"10.1007\/978-3-031-78195-7_30"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8293","DOI":"10.1109\/JBHI.2025.3585379","article-title":"Hybrid Transformer for Early Alzheimer\u2019s Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features","volume":"29","author":"Gong","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_49","unstructured":"Qian, C., and Liao, M. (2021). An Intelligent Screening Mobile Application for Alzheimer\u2019s Disease Using Clock Drawing Test. Proceedings of the SPML \u201921: 2021 4th International Conference on Signal Processing and Machine Learning, Beijing China, 18\u201320 August 2021, Association for Computing Machinery."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Laouedj, S., Wang, Y., Villalba, J., Thebaud, T., Moro-Velazquez, L., and Dehak, N. (2025). Detecting Neurodegenerative Diseases Using Frame-Level Handwriting Embeddings. arXiv.","DOI":"10.1109\/ICASSP49660.2025.10887880"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Trabassi, D., Castiglia, S.F., Bini, F., Marinozzi, F., Ajoudani, A., Lorenzini, M., Chini, G., Varrecchia, T., Ranavolo, A., and De Icco, R. (2024). Optimizing rare disease gait classification through data balancing and generative AI: Insights from hereditary cerebellar ataxia. 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