{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T21:04:59Z","timestamp":1790888699908,"version":"4.1.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"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>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Parkinson\u2019s disease (PD) is a prevalent neurodegenerative disorder that severely affects motor and cognitive functions. Early diagnosis, particularly during the prodromal phase, is critical for effective intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study presents MultimodalCNN-PD++, a deep learning model that integrates Magnetic Resonance Imaging (MRI) with clinical metadata (including motor\/cognitive assessments, demographic data, and genetic biomarkers) to enhance PD classification. The model employs a lightweight EfficientNetB0 backbone, Mobile Convolutional Block Attention Modules (Mobile CBAM), and an enhanced Meta-Guided Cross-Attention (MGCA++) mechanism. A three-stage hierarchical feature selection method identifies the most discriminative clinical features, while metadata is processed with BioClinicalBERT using Low-Rank Adaptation (LoRA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Validated on the Parkinson\u2019s Progression Markers Initiative (PPMI) dataset, the model achieved 97.5% accuracy in distinguishing Normal Control, prodromal PD, and diagnosed PD cases, with reduced parameters and computational costs. External validation on the OASIS-3 dataset confirmed robust generalizability (96.2% accuracy) despite demographic and acquisition protocol variations. Ablation studies highlighted the contributions of Mobile CBAM, MGCA++, hierarchical feature selection, and BioClinicalBERT-LoRA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This framework sets a new benchmark for multiclass PD diagnosis, demonstrating strong potential as a clinically deployable AI tool for early detection and personalized management of neurodegenerative diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1733075","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T07:02:30Z","timestamp":1772002950000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MultimodalCNN-PD: a Parkinson\u2019s disease diagnostics framework using multimodal convolutional neural network"],"prefix":"10.3389","volume":"18","author":[{"given":"Tongle","family":"Zhi","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Yancheng First Hospital Affiliated to Medical School of Nanjing University","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haonan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Yancheng First Hospital Affiliated to Medical School of Nanjing University","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Yancheng First Hospital Affiliated to Medical School of Nanjing University","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar Muhammad","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjie","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Yancheng First Hospital Affiliated to Medical School of Nanjing University","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"B1","volume-title":"Cybernetical Intelligence: Engineering Cybernetics with Machine Intelligence.","author":"Acharya","year":"2024"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e26162","DOI":"10.1016\/j.heliyon.2024.e26162","article-title":"Multimodal abstractive summarization using bidirectional encoder representations from transformers with attention mechanism.","volume":"10","author":"Al-Azzawi","year":"2024","journal-title":"Heliyon"},{"key":"B3","doi-asserted-by":"publisher","first-page":"102295","DOI":"10.1016\/j.compmedimag.2023.102295","article-title":"HViT: Hybrid vision inspired transformer for the assessment of carotid artery plaque by addressing the cross-modality domain adaptation problem in MRI.","volume":"109","author":"Ayoub","year":"2023","journal-title":"Comput. 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