{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:19:24Z","timestamp":1793132364678,"version":"4.5.0"},"reference-count":48,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373116"],"award-info":[{"award-number":["62373116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["QKHZC [2023]118, QKHJCQN [2025]055, and GHJD[2025]"],"award-info":[{"award-number":["QKHZC [2023]118, QKHJCQN [2025]055, and GHJD[2025]"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Although multi-task handwriting analysis has the potential to improve early detection of Alzheimer's disease (AD), the educational bias inherent in its text-based tasks poses a significant obstacle to its widespread adoption across different regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Using the clock drawing test, we aim to design a deep neural network to extract features from static images and process signals to achieve high-precision recognition of early AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Early Detection of Alzheimer's Disease based on Leveraging Multimodal Features of the clock-drawing test (EDADLMF) is proposed. Firstly, to utilize the behavioral features inherent in the clock drawing test task, we propose a Dual Stream Clock Drawing Feature Extraction module,which employs a convolutional neural networks to capture the spatial features of static clock face images, while concurrently employing a multi-layer perceptron to map low-dimensional process signal into a high-dimensional feature space. Furthermore, we propose a Feature Fusion module with the Squeeze-and-Excitation attention mechanism to adaptively enhance key features and fuses complementary information from different modalities. Thirdly, to enhance the model's focus on hard-to-classify samples, a PolyLoss function is introduced to assign greater weights to difficult samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Comparative experiments on benchmark demonstrated that EDADLMF outperforms the compaired methods on accuracy (92.59%), precision (93.65%), recall (92.65%), and F1-score (92.59%), and the case study indicates that the developed prototype system has well effectiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The clock drawing test, combined with process signals and image data, exhibits better screening accuracy and could serve as a practical alternative to initial MRI scans.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261423940","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:45:09Z","timestamp":1773305109000},"page":"1381-1397","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Early detection of Alzheimer's disease based on leveraging multimodal features of the clock drawing test"],"prefix":"10.1177","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7950-7992","authenticated-orcid":false,"given":"Fengying","family":"Yang","sequence":"first","affiliation":[{"name":"Guizhou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5743-3654","authenticated-orcid":false,"given":"Biao","family":"Xu","sequence":"additional","affiliation":[{"name":"Guizhou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1393-5027","authenticated-orcid":false,"given":"Jiacheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Guizhou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5047-3615","authenticated-orcid":false,"given":"Donghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"Rehabilitation Medicine Department, The Third People's Hospital of Guizhou Province, Guiyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4979-0971","authenticated-orcid":false,"given":"Shangen","family":"Lan","sequence":"additional","affiliation":[{"name":"Guizhou University"},{"name":"Innovation Research Institute, Guizhou Communication Industry Service Co., Ltd, Guiyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1512-4799","authenticated-orcid":false,"given":"Kexin","family":"Luo","sequence":"additional","affiliation":[{"name":"Guizhou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8761-5195","authenticated-orcid":false,"given":"Guanci","family":"Yang","sequence":"additional","affiliation":[{"name":"Guizhou University"},{"name":"Guizhou Province International Science &amp; Technology Cooperation Base of Electronic Information and Intelligent Applications, Guizhou University, Guiyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13809"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103836"},{"key":"e_1_3_3_4_2","article-title":"Detection of neurodegenerative disease in brain using region splitting based segmentation with deep unsupervised neural networks","volume":"39","author":"Gandhi CR","year":"2022","unstructured":"Gandhi CR, Murugesh V. 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