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However, existing diagnostic methods often fail to fully exploit multimodal data or systematically incorporate expert domain knowledge. To address these limitations, we propose M\n                    <jats:sup>2<\/jats:sup>\n                    KD-Net, a multimodal and knowledge-driven diagnostic framework that integrates imaging and non-imaging clinical data with structured expert insights to enhance diagnostic performance. The framework consists of three key modules: (1) a contrastive learning-based multimodal feature extractor for improved alignment between imaging and non-imaging data. (2) an expert feature modeling module that encodes domain-specific knowledge through structured annotations, and (3) a cross-modal interaction module that enhances the integration of heterogeneous features across modalities. Experimental results on the Parkinson's Progression Markers Initiative (PPMI) dataset show that M\n                    <jats:sup>2<\/jats:sup>\n                    KD-Net achieves a classification accuracy of 89.6% and an AUC of 0.935 in distinguishing PD patients from healthy controls. This evidence suggests that the developed method provides a dependable, interpretable, and clinically useful solution for PD diagnosis.\n                  <\/jats:p>","DOI":"10.1177\/08953996251358141","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T16:08:06Z","timestamp":1757347686000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["M\n                    <sup>2<\/sup>\n                    KD-Net: A multimodal multi-domain knowledge-driven framework for Parkinson's disease diagnosis"],"prefix":"10.1177","volume":"34","author":[{"given":"Xiangze","family":"Teng","sequence":"first","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China"},{"name":"Qingdao Key Laboratory of Artificial Intelligence Technology for Chinese Medicine, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2374-7708","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China"},{"name":"Qingdao Key Laboratory of Artificial Intelligence Technology for Chinese Medicine, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benzheng","family":"Wei","sequence":"additional","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China"},{"name":"Qingdao Key Laboratory of Artificial Intelligence Technology for Chinese Medicine, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-025-00881-9"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2024.1335550"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/9614163"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.31083\/JIN26393"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.actpsy.2024.104237"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s10194-025-02005-3"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/arclin\/acad061"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.09.008"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.04.049"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-024-00647-9"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2024.3418688"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(23)00404-0"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25116168"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.3233\/JPD-240019"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3233\/XST-16214"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/mds.28706"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.3233\/XST-230426"},{"key":"e_1_3_3_19_2","first-page":"1117","volume-title":"Application of logistic regression algorithm in the diagnosis of expression disorder in Parkinson's disease2021 IEEE 2nd International conference on information technology, big data and artificial intelligence (ICIBA)","volume":"2","author":"Guan Y.","unstructured":"Guan Y. 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