{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:52:25Z","timestamp":1775595145719,"version":"3.50.1"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"SDAIA-KFUPM Joint Research Center for AI","award":["XX1234"],"award-info":[{"award-number":["XX1234"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1186\/s40708-025-00291-w","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T17:05:31Z","timestamp":1770051931000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multimodal fusion and explainability of artificial intelligence models in Alzheimer\u2019s Disease detection"],"prefix":"10.1186","volume":"13","author":[{"given":"Vimbi","family":"Viswan","sequence":"first","affiliation":[]},{"given":"Noushath","family":"Shaffi","sequence":"additional","affiliation":[]},{"given":"E.","family":"Malathy","sequence":"additional","affiliation":[]},{"given":"G.","family":"Chemmalar Selvi","sequence":"additional","affiliation":[]},{"given":"B. R.","family":"Kavitha","sequence":"additional","affiliation":[]},{"given":"Abdelhamid","family":"Abdesselam","sequence":"additional","affiliation":[]},{"given":"Shuqiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ponnuthurai N.","family":"Suganthan","sequence":"additional","affiliation":[]},{"given":"Ibrahim Al","family":"Shezawi","sequence":"additional","affiliation":[]},{"given":"Mufti","family":"Mahmud","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"291_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-020-00112-2","volume":"7","author":"MBT Noor","year":"2020","unstructured":"Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of alzheimer\u2019s disease, parkinson\u2019s disease and schizophrenia. Brain Inform 7:1\u201321","journal-title":"Brain Inform"},{"key":"291_CR2","unstructured":"Gauthier S, Webster C, Sarvaes S, Morais J, Rosa-Neto P (2022) World Alzheimer Report 2022: Life After Diagnosis - Navigating Treatment, Care and Support"},{"issue":"1","key":"291_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-023-00184-w","volume":"10","author":"F Hajamohideen","year":"2023","unstructured":"Hajamohideen F, Shaffi N, Mahmud M, Subramanian K, Al Sariri A, Vimbi V, Abdesselam A (2023) Four-way classification of alzheimer\u2019s disease using deep siamese convolutional neural network with triplet-loss function. Brain Inform 10(1):1\u201313","journal-title":"Brain Inform"},{"key":"291_CR4","doi-asserted-by":"crossref","unstructured":"Shaffi N, Hajamohideen F, Abdesselam A, Mahmud M, et al (2023) Ensemble classifiers for a 4-way classification of alzheimer\u2019s disease. In: Proc.AII2022, pp. 219\u2013230","DOI":"10.1007\/978-3-031-24801-6_16"},{"key":"291_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-020-09773-x","volume":"13","author":"M Mahmud","year":"2021","unstructured":"Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13:1\u201333","journal-title":"Cogn Comput"},{"issue":"6","key":"291_CR6","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1109\/TNNLS.2018.2790388","volume":"29","author":"M Mahmud","year":"2018","unstructured":"Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063\u20132079","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"291_CR7","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40708-024-00222-1","volume":"11","author":"V Vimbi","year":"2024","unstructured":"Vimbi V, Shaffi N, Mahmud M (2024) Interpreting artificial intelligence models: a systematic review on the application of lime and shap in alzheimer\u2019s disease detection. Brain Inform 11(1):10","journal-title":"Brain Inform"},{"key":"291_CR8","doi-asserted-by":"crossref","unstructured":"Viswan V, Shaffi N, Mahmud M, Subramanian K, Hajamohideen F (2023) Explainable artificial intelligence in alzheimer\u2019s disease classification: a systematic review. Cogn Comput 1\u201344","DOI":"10.1007\/s12559-023-10192-x"},{"issue":"3","key":"291_CR9","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.3390\/eng5030078","volume":"5","author":"M Aviles","year":"2024","unstructured":"Aviles M, S\u00e1nchez-Reyes LM, \u00c1lvarez-Alvarado JM, Rodr\u00edguez-Res\u00e9ndiz J (2024) Machine and deep learning trends in eeg-based detection and diagnosis of alzheimer\u2019s disease: a systematic review. Eng 5(3):1464\u20131484","journal-title":"Eng"},{"issue":"9","key":"291_CR10","doi-asserted-by":"publisher","first-page":"70025","DOI":"10.1002\/hsr2.70025","volume":"7","author":"MK Awang","year":"2024","unstructured":"Awang MK, Ali G, Faheem M (2024) Deep learning techniques for alzheimer\u2019s disease detection in 3d imaging: a systematic review. Health Sci Rep 7(9):70025","journal-title":"Health Sci Rep"},{"key":"291_CR11","doi-asserted-by":"crossref","unstructured":"Teles AS, de Moura IR, Silva F, Roberts A, Stahl D (2025) Ehr-based prediction modelling meets multimodal deep learning: a systematic review of structured and textual data fusion methods. Inform Fusion 102981","DOI":"10.1016\/j.inffus.2025.102981"},{"issue":"1","key":"291_CR12","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3390\/make6010024","volume":"6","author":"MG Alsubaie","year":"2024","unstructured":"Alsubaie MG, Luo S, Shaukat K (2024) Alzheimer\u2019s disease detection using deep learning on neuroimaging: a systematic review. Mach Learn Knowl Extr 6(1):464\u2013505","journal-title":"Mach Learn Knowl Extr"},{"issue":"1","key":"291_CR13","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1111\/j.1749-6632.2009.04420.x","volume":"1156","author":"PA Bandettini","year":"2009","unstructured":"Bandettini PA (2009) What\u2019s new in neuroimaging methods? Ann N Y Acad Sci 1156(1):260\u2013293","journal-title":"Ann N Y Acad Sci"},{"issue":"2","key":"291_CR14","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/34.982906","volume":"24","author":"LI Kuncheva","year":"2002","unstructured":"Kuncheva LI (2002) A theoretical study on six classifier fusion strategies. IEEE Trans Pattern Anal Mach Intell 24(2):281\u2013286","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"291_CR15","unstructured":"Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report. http:\/\/www.dur.ac.uk\/ebse\/resources\/Systematic-reviews-5-8.pdf"},{"issue":"1","key":"291_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13643-021-01626-4","volume":"10","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE et al (2021) The prisma 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10(1):1\u201311","journal-title":"Syst Rev"},{"key":"291_CR17","doi-asserted-by":"crossref","unstructured":"Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, Topol EJ, Ioannidis JP, Collins GS, Maruthappu M (2020) Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. bmj 368","DOI":"10.1136\/bmj.m689"},{"key":"291_CR18","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.neunet.2019.12.006","volume":"123","author":"C Ieracitano","year":"2020","unstructured":"Ieracitano C, Mammone N, Hussain A, Morabito FC (2020) A novel multi-modal machine learning based approach for automatic classification of eeg recordings in dementia. Neural Netw 123:176\u2013190","journal-title":"Neural Netw"},{"key":"291_CR19","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.media.2018.11.006","volume":"52","author":"Y Li","year":"2019","unstructured":"Li Y, Liu J, Gao X, Jie B, Kim M, Yap P-T, Wee C-Y, Shen D (2019) Multimodal hyper-connectivity of functional networks using functionally-weighted lasso for mci classification. Med Image Anal 52:80\u201396","journal-title":"Med Image Anal"},{"issue":"9","key":"291_CR20","doi-asserted-by":"publisher","first-page":"3161","DOI":"10.1109\/TMI.2024.3386937","volume":"43","author":"Z Qiu","year":"2024","unstructured":"Qiu Z, Yang P, Xiao C, Wang S, Xiao X, Qin J, Liu C-M, Wang T, Lei B (2024) 3d multimodal fusion network with disease-induced joint learning for early alzheimer\u2019s disease diagnosis. IEEE Trans Med Imaging 43(9):3161\u20133175","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"291_CR21","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","volume":"22","author":"J Shi","year":"2017","unstructured":"Shi J, Zheng X, Li Y, Zhang Q, Ying S (2017) Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of alzheimer\u2019s disease. IEEE J Biomed Health Inform 22(1):173\u2013183","journal-title":"IEEE J Biomed Health Inform"},{"key":"291_CR22","doi-asserted-by":"publisher","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","volume":"7","author":"C Feng","year":"2019","unstructured":"Feng C, Elazab A, Yang P, Wang T, Zhou F, Hu H, Xiao X, Lei B (2019) Deep learning framework for alzheimer\u2019s disease diagnosis via 3d-cnn and fsbi-lstm. IEEE Access 7:63605\u201363618","journal-title":"IEEE Access"},{"key":"291_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106116","volume":"150","author":"A Dong","year":"2022","unstructured":"Dong A, Zhang G, Liu J, Wei Z (2022) Latent feature representation learning for alzheimer\u2019s disease classification. Comput Biol Med 150:106116","journal-title":"Comput Biol Med"},{"key":"291_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102698","volume":"84","author":"Z Chen","year":"2023","unstructured":"Chen Z, Liu Y, Zhang Y, Li Q, Initiative ADN et al (2023) Orthogonal latent space learning with feature weighting and graph learning for multimodal alzheimer\u2019s disease diagnosis. Med Image Anal 84:102698","journal-title":"Med Image Anal"},{"key":"291_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104312","volume":"80","author":"VS Rallabandi","year":"2023","unstructured":"Rallabandi VS, Seetharaman K (2023) Deep learning-based classification of healthy aging controls, mild cognitive impairment and alzheimer\u2019s disease using fusion of mri-pet imaging. Biomed Signal Process Control 80:104312","journal-title":"Biomed Signal Process Control"},{"key":"291_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103565","volume":"75","author":"Z Kong","year":"2022","unstructured":"Kong Z, Zhang M, Zhu W, Yi Y, Wang T, Zhang B (2022) Multi-modal data alzheimer\u2019s disease detection based on 3d convolution. Biomed Signal Process Control 75:103565","journal-title":"Biomed Signal Process Control"},{"key":"291_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106788","volume":"157","author":"Y Leng","year":"2023","unstructured":"Leng Y, Cui W, Peng Y, Yan C, Cao Y, Yan Z, Chen S, Jiang X, Zheng J, Initiative ADN et al (2023) Multimodal cross enhanced fusion network for diagnosis of alzheimer\u2019s disease and subjective memory complaints. Comput Biol Med 157:106788","journal-title":"Comput Biol Med"},{"issue":"1","key":"291_CR28","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3390\/brainsci12010080","volume":"12","author":"Z Jiao","year":"2022","unstructured":"Jiao Z, Chen S, Shi H, Xu J (2022) Multi-modal feature selection with feature correlation and feature structure fusion for mci and ad classification. Brain Sci 12(1):80","journal-title":"Brain Sci"},{"issue":"6","key":"291_CR29","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1109\/TMI.2021.3063150","volume":"40","author":"Z Ning","year":"2021","unstructured":"Ning Z, Xiao Q, Feng Q, Chen W, Zhang Y (2021) Relation-induced multi-modal shared representation learning for alzheimer\u2019s disease diagnosis. IEEE Trans Med Imaging 40(6):1632\u20131645","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"291_CR30","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.neuroimage.2011.09.069","volume":"59","author":"D Zhang","year":"2012","unstructured":"Zhang D, Shen D, Initiative ADN et al (2012) Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in alzheimer\u2019s disease. Neuroimage 59(2):895\u2013907","journal-title":"Neuroimage"},{"key":"291_CR31","doi-asserted-by":"crossref","unstructured":"Khan AA, Mahendran RK, Perumal K, Faheem M (2024) Dual-3dm 3-ad: Mixed transformer based semantic segmentation and triplet pre-processing for early multi-class alzheimer\u2019s diagnosis. IEEE Trans Neural Syst Rehabilitation Eng","DOI":"10.1109\/TNSRE.2024.3357723"},{"key":"291_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109438","volume":"184","author":"M Abdelaziz","year":"2025","unstructured":"Abdelaziz M, Wang T, Anwaar W, Elazab A (2025) Multi-scale multimodal deep learning framework for alzheimer\u2019s disease diagnosis. Comput Biol Med 184:109438","journal-title":"Comput Biol Med"},{"key":"291_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102643","volume":"82","author":"L Xu","year":"2022","unstructured":"Xu L, Wu H, He C, Wang J, Zhang C, Nie F, Chen L (2022) Multi-modal sequence learning for alzheimer\u2019s disease progression prediction with incomplete variable-length longitudinal data. Med Image Anal 82:102643","journal-title":"Med Image Anal"},{"key":"291_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104400","volume":"80","author":"F Liu","year":"2023","unstructured":"Liu F, Yuan S, Li W, Xu Q, Sheng B (2023) Patch-based deep multi-modal learning framework for alzheimer\u2019s disease diagnosis using multi-view neuroimaging. Biomed Signal Process Control 80:104400","journal-title":"Biomed Signal Process Control"},{"key":"291_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107085","volume":"100","author":"H Zhang","year":"2025","unstructured":"Zhang H, Ni M, Yang Y, Xie F, Wang W, He Y, Chen W, Chen Z (2025) Patch-based interpretable deep learning framework for alzheimer\u2019s disease diagnosis using multimodal data. Biomed Signal Process Control 100:107085","journal-title":"Biomed Signal Process Control"},{"key":"291_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102543","volume":"140","author":"M Eslami","year":"2023","unstructured":"Eslami M, Tabarestani S, Adjouadi M (2023) A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of alzheimer\u2019s disease. Artif Intell Med 140:102543","journal-title":"Artif Intell Med"},{"issue":"5","key":"291_CR37","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3390\/diagnostics15050612","volume":"15","author":"M Taiyeb Khosroshahi","year":"2025","unstructured":"Taiyeb Khosroshahi M, Morsali S, Gharakhanlou S, Motamedi A, Hassanbaghlou S, Vahedi H, Pedrammehr S, Kabir HMD, Jafarizadeh A (2025) Explainable artificial intelligence in neuroimaging of alzheimer\u2019s disease. Diagnostics 15(5):612","journal-title":"Diagnostics"},{"key":"291_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108564","volume":"176","author":"X Liu","year":"2024","unstructured":"Liu X, Li W, Miao S, Liu F, Han K, Bezabih TT (2024) Hammf: hierarchical attention-based multi-task and multi-modal fusion model for computer-aided diagnosis of alzheimer\u2019s disease. Comput Biol Med 176:108564","journal-title":"Comput Biol Med"},{"key":"291_CR39","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.nicl.2013.05.004","volume":"2","author":"J Young","year":"2013","unstructured":"Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S, Initiative ADN et al (2013) Accurate multimodal probabilistic prediction of conversion to alzheimer\u2019s disease in patients with mild cognitive impairment. NeuroImage Clinical 2:735\u2013745","journal-title":"NeuroImage Clinical"},{"issue":"10","key":"291_CR40","doi-asserted-by":"publisher","first-page":"2411","DOI":"10.1109\/TMI.2019.2913158","volume":"38","author":"T Zhou","year":"2019","unstructured":"Zhou T, Liu M, Thung K-H, Shen D (2019) Latent representation learning for alzheimer\u2019s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans Med Imaging 38(10):2411\u20132422","journal-title":"IEEE Trans Med Imaging"},{"key":"291_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2022.109582","volume":"375","author":"M Aghili","year":"2022","unstructured":"Aghili M, Tabarestani S, Adjouadi M (2022) Addressing the missing data challenge in multi-modal datasets for the diagnosis of alzheimer\u2019s disease. J Neurosci Methods 375:109582","journal-title":"J Neurosci Methods"},{"key":"291_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105669","volume":"88","author":"P Lu","year":"2024","unstructured":"Lu P, Hu L, Mitelpunkt A, Bhatnagar S, Lu L, Liang H (2024) A hierarchical attention-based multimodal fusion framework for predicting the progression of alzheimer\u2019s disease. Biomed Signal Process Control 88:105669","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"291_CR43","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1186\/s12967-024-05025-w","volume":"22","author":"Y Wang","year":"2024","unstructured":"Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z, Initiative ADN (2024) Predicting long-term progression of alzheimer\u2019s disease using a multimodal deep learning model incorporating interaction effects. J Transl Med 22(1):265","journal-title":"J Transl Med"},{"key":"291_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103213","volume":"97","author":"B Lei","year":"2024","unstructured":"Lei B, Li Y, Fu W, Yang P, Chen S, Wang T, Xiao X, Niu T, Fu Y, Wang S et al (2024) Alzheimer\u2019s disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network. Med Image Anal 97:103213","journal-title":"Med Image Anal"},{"issue":"1","key":"291_CR45","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1038\/s41598-021-82098-3","volume":"11","author":"S El-Sappagh","year":"2021","unstructured":"El-Sappagh S, Alonso JM, Islam SR, Sultan AM, Kwak KS (2021) A multilayer multimodal detection and prediction model based on explainable artificial intelligence for alzheimer\u2019s disease. Sci Rep 11(1):2660","journal-title":"Sci Rep"},{"key":"291_CR46","doi-asserted-by":"publisher","first-page":"1557177","DOI":"10.3389\/fninf.2025.1557177","volume":"19","author":"ML Raza","year":"2025","unstructured":"Raza ML, Hassan ST, Jamil S, Hyder N, Batool K, Walji S, Abbas MK (2025) Advancements in deep learning for early diagnosis of alzheimer\u2019s disease using multimodal neuroimaging: challenges and future directions. Front Neuroinform 19:1557177","journal-title":"Front Neuroinform"},{"key":"291_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108740","volume":"178","author":"M Zhang","year":"2024","unstructured":"Zhang M, Cui Q, L\u00fc Y, Li W (2024) A feature-aware multimodal framework with auto-fusion for alzheimer\u2019s disease diagnosis. Comput Biol Med 178:108740","journal-title":"Comput Biol Med"},{"issue":"8","key":"291_CR48","doi-asserted-by":"publisher","first-page":"4993","DOI":"10.1016\/j.jksuci.2020.12.009","volume":"34","author":"MN KP","year":"2022","unstructured":"KP MN, Thiyagarajan P (2022) Feature selection using efficient fusion of fisher score and greedy searching for alzheimer\u2019s classification. J King Saud Univ-Comput Inf Sci 34(8):4993\u20135006","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"291_CR49","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2020.05.087","volume":"412","author":"S El-Sappagh","year":"2020","unstructured":"El-Sappagh S, Abuhmed T, Islam SR, Kwak KS (2020) Multimodal multitask deep learning model for alzheimer\u2019s disease progression detection based on time series data. Neurocomputing 412:197\u2013215","journal-title":"Neurocomputing"},{"key":"291_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106688","volume":"213","author":"T Abuhmed","year":"2021","unstructured":"Abuhmed T, El-Sappagh S, Alonso JM (2021) Robust hybrid deep learning models for alzheimer\u2019s progression detection. Knowl-Based Syst 213:106688","journal-title":"Knowl-Based Syst"},{"issue":"17","key":"291_CR51","doi-asserted-by":"publisher","first-page":"14487","DOI":"10.1007\/s00521-022-07263-9","volume":"34","author":"S El-Sappagh","year":"2022","unstructured":"El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T (2022) Two-stage deep learning model for alzheimer\u2019s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 34(17):14487\u201314509","journal-title":"Neural Comput Appl"},{"key":"291_CR52","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neucom.2022.09.009","volume":"512","author":"S El-Sappagh","year":"2022","unstructured":"El-Sappagh S, Ali F, Abuhmed T, Singh J, Alonso JM (2022) Automatic detection of alzheimer\u2019s disease progression: an efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 512:203\u2013224","journal-title":"Neurocomputing"},{"key":"291_CR53","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2017.12.005","volume":"302","author":"J Ram\u00edrez","year":"2018","unstructured":"Ram\u00edrez J, G\u00f3rriz J, Ortiz A, Mart\u00ednez-Murcia F, Segovia F, Salas-Gonzalez D, Castillo-Barnes D, Ill\u00e1n I, Puntonet C, Initiative ADN et al (2018) Ensemble of random forests one vs. rest classifiers for mci and ad prediction using anova cortical and subcortical feature selection and partial least squares. J Neurosci Methods 302:47\u201357","journal-title":"J Neurosci Methods"},{"key":"291_CR54","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jneumeth.2018.03.008","volume":"302","author":"D Yao","year":"2018","unstructured":"Yao D, Calhoun VD, Fu Z, Du Y, Sui J (2018) An ensemble learning system for a 4-way classification of alzheimer\u2019s disease and mild cognitive impairment. J Neurosci Methods 302:75\u201381","journal-title":"J Neurosci Methods"},{"key":"291_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118586","volume":"244","author":"H Guan","year":"2021","unstructured":"Guan H, Wang C, Tao D (2021) Mri-based alzheimer\u2019s disease prediction via distilling the knowledge in multi-modal data. Neuroimage 244:118586","journal-title":"Neuroimage"},{"key":"291_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105901","volume":"148","author":"Y Tu","year":"2022","unstructured":"Tu Y, Lin S, Qiao J, Zhuang Y, Zhang P (2022) Alzheimer\u2019s disease diagnosis via multimodal feature fusion. Comput Biol Med 148:105901","journal-title":"Comput Biol Med"},{"key":"291_CR57","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.inffus.2022.11.028","volume":"92","author":"N Rahim","year":"2023","unstructured":"Rahim N, El-Sappagh S, Ali S, Muhammad K, Del Ser J, Abuhmed T (2023) Prediction of alzheimer\u2019s progression based on multimodal deep-learning-based fusion and visual explainability of time-series data. Information Fusion 92:363\u2013388","journal-title":"Information Fusion"},{"key":"291_CR58","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.neurobiolaging.2022.10.005","volume":"121","author":"G Mirabnahrazam","year":"2023","unstructured":"Mirabnahrazam G, Ma D, Beaulac C, Lee S, Popuri K, Lee H, Cao J, Galvin JE, Wang L, Beg MF et al (2023) Predicting time-to-conversion for dementia of alzheimer\u2019s type using multi-modal deep survival analysis. Neurobiol Aging 121:139\u2013156","journal-title":"Neurobiol Aging"},{"key":"291_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115549","volume":"185","author":"JE Arco","year":"2021","unstructured":"Arco JE, Ram\u00edrez J, G\u00f3rriz JM, Ruz M, Initiative ADN et al (2021) Data fusion based on searchlight analysis for the prediction of alzheimer\u2019s disease. Expert Syst Appl 185:115549","journal-title":"Expert Syst Appl"},{"issue":"10","key":"291_CR60","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1038\/s41591-024-03118-z","volume":"30","author":"C Xue","year":"2024","unstructured":"Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, Walia AS, Guney OB, Zhang JD, Po\u00e9sy S et al (2024) Ai-based differential diagnosis of dementia etiologies on multimodal data. Nat Med 30(10):2977\u20132989","journal-title":"Nat Med"},{"key":"291_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2024.110625","volume":"197","author":"M Zhang","year":"2024","unstructured":"Zhang M, Cui Q, L\u00fc Y, Yu W, Li W (2024) A multimodal learning machine framework for alzheimer\u2019s disease diagnosis based on neuropsychological and neuroimaging data. Comput Ind Eng 197:110625","journal-title":"Comput Ind Eng"},{"key":"291_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103214","volume":"71","author":"S Goyal","year":"2022","unstructured":"Goyal S, Singh V, Rani A, Yadav N (2022) Multimodal image fusion and denoising in nsct domain using cnn and fotgv. Biomed Signal Process Control 71:103214","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"291_CR63","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","volume":"62","author":"S Liu","year":"2014","unstructured":"Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ et al (2014) Multimodal neuroimaging feature learning for multiclass diagnosis of alzheimer\u2019s disease. IEEE Trans Biomed Eng 62(4):1132\u20131140","journal-title":"IEEE Trans Biomed Eng"},{"key":"291_CR64","doi-asserted-by":"crossref","unstructured":"Zhu, Q, Xu B, Huang J, Wang H, Xu R, Shao W, Zhang D (2022) Deep multi-modal discriminative and interpretability network for alzheimer\u2019s disease diagnosis. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2022.3230750"},{"key":"291_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2023.100749","volume":"27","author":"M Leela","year":"2023","unstructured":"Leela M, Helenprabha K, Sharmila L (2023) Prediction and classification of alzheimer disease categories using integrated deep transfer learning approach. Measurement Sensors 27:100749","journal-title":"Measurement Sensors"},{"key":"291_CR66","doi-asserted-by":"crossref","unstructured":"Mustafa Y, Luo T (2024). Unmasking dementia detection by masking input gradients: a jsm approach to model interpretability and precision. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp 75\u201390","DOI":"10.1007\/978-981-97-2259-4_6"},{"issue":"4","key":"291_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102965","volume":"59","author":"J Ma","year":"2022","unstructured":"Ma J, Zhang J, Wang Z (2022) Multimodality alzheimer\u2019s disease analysis in deep riemannian manifold. Inf Process Manag 59(4):102965","journal-title":"Inf Process Manag"},{"key":"291_CR68","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.future.2020.10.005","volume":"115","author":"S El-Sappagh","year":"2021","unstructured":"El-Sappagh S, Saleh H, Sahal R, Abuhmed T, Islam SR, Ali F, Amer E (2021) Alzheimer\u2019s disease progression detection model based on an early fusion of cost-effective multimodal data. Futur Gener Comput Syst 115:680\u2013699","journal-title":"Futur Gener Comput Syst"},{"key":"291_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106201","volume":"151","author":"M Velazquez","year":"2022","unstructured":"Velazquez M, Lee Y (2022) Multimodal ensemble model for alzheimer\u2019s disease conversion prediction from early mild cognitive impairment subjects. Comput Biol Med 151:106201","journal-title":"Comput Biol Med"},{"key":"291_CR70","doi-asserted-by":"crossref","unstructured":"Aderghal K, Afdel K, Benois-Pineau J, Catheline G (2020) Improving alzheimer\u2019s stage categorization with convolutional neural network using transfer learning and different magnetic resonance imaging modalities. Heliyon 6(12)","DOI":"10.1016\/j.heliyon.2020.e05652"},{"issue":"8","key":"291_CR71","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1016\/j.mri.2016.05.001","volume":"34","author":"X Tang","year":"2016","unstructured":"Tang X, Qin Y, Wu J, Zhang M, Zhu W, Miller MI (2016) Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in alzheimer\u2019s disease. Magn Reson Imaging 34(8):1087\u20131099","journal-title":"Magn Reson Imaging"},{"key":"291_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108795","volume":"341","author":"T Zhang","year":"2020","unstructured":"Zhang T, Shi M (2020) Multi-modal neuroimaging feature fusion for diagnosis of alzheimer\u2019s disease. J Neurosci Methods 341:108795","journal-title":"J Neurosci Methods"},{"issue":"1","key":"291_CR73","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1186\/s12967-024-05025-w","volume":"22","author":"Y Wang","year":"2024","unstructured":"Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z, Initiative ADN (2024) Predicting long-term progression of alzheimer\u2019s disease using a multimodal deep learning model incorporating interaction effects. J Transl Med 22(1):265","journal-title":"J Transl Med"},{"key":"291_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105669","volume":"88","author":"P Lu","year":"2024","unstructured":"Lu P, Hu L, Mitelpunkt A, Bhatnagar S, Lu L, Liang H (2024) A hierarchical attention-based multimodal fusion framework for predicting the progression of alzheimer\u2019s disease. Biomed Signal Process Control 88:105669","journal-title":"Biomed Signal Process Control"},{"key":"291_CR75","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2019) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vision. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int J Comput Vision"},{"issue":"8","key":"291_CR76","doi-asserted-by":"publisher","first-page":"3846","DOI":"10.3390\/app12083846","volume":"12","author":"J An","year":"2022","unstructured":"An J, Joe I (2022) Attention map-guided visual explanations for deep neural networks. Appl Sci 12(8):3846","journal-title":"Appl Sci"},{"key":"291_CR77","unstructured":"Touvron H, Cord M, Douze M, et al (2021) Training data-efficient image transformers & distillation through attention. In: Proc. ICML, pp. 10347\u201310357"},{"issue":"7","key":"291_CR78","doi-asserted-by":"publisher","first-page":"0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach S, Binder A, Montavon G, Klauschen F, M\u00fcller K-R, Samek W (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7):0130140","journal-title":"PLoS ONE"},{"key":"291_CR79","unstructured":"Bazen S, Joutard X (2013) The taylor decomposition: a unified generalization of the oaxaca method to nonlinear models"},{"key":"291_CR80","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201c why should i trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"291_CR81","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/S1364-6613(00)01817-9","volume":"6","author":"Z Li","year":"2002","unstructured":"Li Z (2002) A saliency map in primary visual cortex. Trends Cogn Sci 6(1):9\u201316","journal-title":"Trends Cogn Sci"},{"key":"291_CR82","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107495","volume":"234","author":"M Junaid","year":"2023","unstructured":"Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T (2023) Explainable machine learning models based on multimodal time-series data for the early detection of parkinson\u2019s disease. Comput Methods Programs Biomed 234:107495","journal-title":"Comput Methods Programs Biomed"},{"issue":"9","key":"291_CR83","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/JPROC.2015.2460697","volume":"103","author":"D Lahat","year":"2015","unstructured":"Lahat D, Adali T, Jutten C (2015) Multimodal data fusion: an overview of methods, challenges, and prospects. Proc IEEE 103(9):1449\u20131477","journal-title":"Proc IEEE"},{"key":"291_CR84","doi-asserted-by":"publisher","first-page":"23825","DOI":"10.1007\/s11042-018-5696-z","volume":"77","author":"F Malawski","year":"2018","unstructured":"Malawski F, Ga\u0142ka J (2018) System for multimodal data acquisition for human action recognition. Multimedia Tools Appl 77:23825\u201323850","journal-title":"Multimedia Tools Appl"},{"key":"291_CR85","doi-asserted-by":"publisher","first-page":"5175","DOI":"10.1109\/TSP.2021.3109375","volume":"69","author":"Y Yilmaz","year":"2021","unstructured":"Yilmaz Y, Aktukmak M, Hero AO (2021) Multimodal data fusion in high-dimensional heterogeneous datasets via generative models. IEEE Trans Signal Process 69:5175\u20135188","journal-title":"IEEE Trans Signal Process"},{"key":"291_CR86","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.741489","volume":"15","author":"A Safai","year":"2022","unstructured":"Safai A, Vakharia N, Prasad S, Saini J, Shah A, Lenka A, Pal PK, Ingalhalikar M (2022) Multimodal brain connectomics-based prediction of parkinson\u2019s disease using graph attention networks. Front Neurosci 15:741489","journal-title":"Front Neurosci"},{"issue":"6","key":"291_CR87","doi-asserted-by":"publisher","first-page":"399","DOI":"10.2217\/cns.15.20","volume":"4","author":"NR Boonzaier","year":"2015","unstructured":"Boonzaier NR, Piccirillo SG, Watts C, Price SJ (2015) Assessing and monitoring intratumor heterogeneity in glioblastoma: how far has multimodal imaging come? CNS Oncology 4(6):399\u2013410","journal-title":"CNS Oncology"},{"key":"291_CR88","first-page":"1","volume":"61","author":"L Li","year":"2023","unstructured":"Li L, Han L, Ding M, Cao H (2023) Multimodal image fusion framework for end-to-end remote sensing image registration. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"291_CR89","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s12193-012-0101-0","volume":"7","author":"D Sanchez-Cortes","year":"2013","unstructured":"Sanchez-Cortes D, Aran O, Jayagopi DB, Schmid Mast M, Gatica-Perez D (2013) Emergent leaders through looking and speaking: from audio-visual data to multimodal recognition. J Multimodal User Interfaces 7:39\u201353","journal-title":"J Multimodal User Interfaces"},{"key":"291_CR90","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.inffus.2019.02.007","volume":"51","author":"J Guo","year":"2019","unstructured":"Guo J, Song B, Zhang P, Ma M, Luo W et al (2019) Affective video content analysis based on multimodal data fusion in heterogeneous networks. Inf Fusion 51:224\u2013232","journal-title":"Inf Fusion"},{"key":"291_CR91","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.012","volume":"73","author":"X Jiang","year":"2021","unstructured":"Jiang X, Ma J, Xiao G, Shao Z, Guo X (2021) A review of multimodal image matching: methods and applications. Information Fusion 73:22\u201371","journal-title":"Information Fusion"},{"key":"291_CR92","doi-asserted-by":"crossref","unstructured":"Spasov SE, Passamonti L, Duggento A, Lio P, Toschi N (2018) A multi-modal convolutional neural network framework for the prediction of alzheimer\u2019s disease. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1271\u20131274. IEEE","DOI":"10.1109\/EMBC.2018.8512468"},{"key":"291_CR93","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2019.06.051","volume":"362","author":"M Lv","year":"2019","unstructured":"Lv M, Xu W, Chen T (2019) A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors. Neurocomputing 362:33\u201340","journal-title":"Neurocomputing"},{"issue":"2","key":"291_CR94","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TCBB.2021.3083566","volume":"19","author":"J Gao","year":"2021","unstructured":"Gao J, Lyu T, Xiong F, Wang J, Ke W, Li Z (2021) Predicting the survival of cancer patients with multimodal graph neural network. IEEE\/ACM Trans Comput Biol Bioinf 19(2):699\u2013709","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"291_CR95","doi-asserted-by":"crossref","unstructured":"Xu P, Zhu X, Clifton DA (2023) Multimodal learning with transformers: A survey. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2023.3275156"},{"issue":"5\u20136","key":"291_CR96","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1080\/01691864.2022.2035253","volume":"36","author":"M Suzuki","year":"2022","unstructured":"Suzuki M, Matsuo Y (2022) A survey of multimodal deep generative models. Adv Robot 36(5\u20136):261\u2013278","journal-title":"Adv Robot"},{"key":"291_CR97","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.specom.2022.09.001","volume":"145","author":"PA P\u00e9rez-Toro","year":"2022","unstructured":"P\u00e9rez-Toro PA, Arias-Vergara T, Klumpp P, V\u00e1squez-Correa JC, Schuster M, Noeth E, Orozco-Arroyave JR (2022) Depression assessment in people with parkinson\u2019s disease: the combination of acoustic features and natural language processing. Speech Commun 145:10\u201320","journal-title":"Speech Commun"},{"key":"291_CR98","doi-asserted-by":"crossref","unstructured":"Xue Z, Marculescu R (2023) Dynamic multimodal fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 2574\u20132583","DOI":"10.1109\/CVPRW59228.2023.00256"},{"issue":"4","key":"291_CR99","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1038\/s42256-023-00633-5","volume":"5","author":"S Steyaert","year":"2023","unstructured":"Steyaert S, Pizurica M, Nagaraj D, Khandelwal P, Hernandez-Boussard T, Gentles AJ, Gevaert O (2023) Multimodal data fusion for cancer biomarker discovery with deep learning. Nat Mach Intell 5(4):351\u2013362","journal-title":"Nat Mach Intell"},{"key":"291_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104561","volume":"82","author":"M Fang","year":"2023","unstructured":"Fang M, Peng S, Liang Y, Hung C-C, Liu S (2023) A multimodal fusion model with multi-level attention mechanism for depression detection. Biomed Signal Process Control 82:104561","journal-title":"Biomed Signal Process Control"},{"key":"291_CR101","doi-asserted-by":"crossref","unstructured":"Grassucci E, Sigillo L, Uncini A, Comminiello D (2023) Grouse: a task and model agnostic wavelet-driven framework for medical imaging. IEEE Signal Process","DOI":"10.1109\/LSP.2023.3321554"},{"key":"291_CR102","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.inffus.2022.11.028","volume":"92","author":"N Rahim","year":"2023","unstructured":"Rahim N, El-Sappagh S, Ali S, Muhammad K, Del Ser J, Abuhmed T (2023) Prediction of alzheimer\u2019s progression based on multimodal deep-learning-based fusion and visual explainability of time-series data. Information Fusion 92:363\u2013388","journal-title":"Information Fusion"},{"key":"291_CR103","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101955","volume":"100","author":"D Folgado","year":"2023","unstructured":"Folgado D, Barandas M, Famiglini L, Santos R, Cabitza F, Gamboa H (2023) Explainability meets uncertainty quantification: insights from feature-based model fusion on multimodal time series. Information Fusion 100:101955","journal-title":"Information Fusion"},{"issue":"7","key":"291_CR104","doi-asserted-by":"publisher","first-page":"3287","DOI":"10.1007\/s00432-022-04180-1","volume":"149","author":"Q Chen","year":"2023","unstructured":"Chen Q, Li M, Chen C, Zhou P, Lv X, Chen C (2023) Mdfnet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification. J Cancer Res Clin Oncol 149(7):3287\u20133299","journal-title":"J Cancer Res Clin Oncol"},{"issue":"4","key":"291_CR105","doi-asserted-by":"publisher","first-page":"3639","DOI":"10.1007\/s10489-022-03610-4","volume":"53","author":"P Zhou","year":"2023","unstructured":"Zhou P, Chen H, Li Y, Peng Y (2023) Unpaired multi-modal tumor segmentation with structure adaptation. Appl Intell 53(4):3639\u20133651","journal-title":"Appl Intell"},{"issue":"1","key":"291_CR106","doi-asserted-by":"publisher","first-page":"3404","DOI":"10.1038\/s41467-022-31037-5","volume":"13","author":"S Qiu","year":"2022","unstructured":"Qiu S, Miller MI, Joshi PS, Lee JC, Xue C, Ni Y, Wang Y, De Anda-Duran I, Hwang PH, Cramer JA et al (2022) Multimodal deep learning for alzheimer\u2019s disease dementia assessment. Nat Commun 13(1):3404","journal-title":"Nat Commun"},{"key":"291_CR107","doi-asserted-by":"crossref","unstructured":"Koksalmis GH, Soykan B, Brattain LJ, Huang H-H (2025) Artificial intelligence for personalized prediction of alzheimer\u2019s disease progression: A survey of methods, data challenges, and future directions. arXiv preprint arXiv:2504.21189","DOI":"10.1002\/wics.70043"},{"key":"291_CR108","doi-asserted-by":"crossref","unstructured":"Leony F, Lin C-J, Initiative ADN et al (2025) Multimodal fusion architectures for alzheimer\u2019s disease diagnosis: an experimental study. J Biomed Inform 104834","DOI":"10.1016\/j.jbi.2025.104834"},{"key":"291_CR109","doi-asserted-by":"publisher","first-page":"1557177","DOI":"10.3389\/fninf.2025.1557177","volume":"19","author":"ML Raza","year":"2025","unstructured":"Raza ML, Hassan ST, Jamil S, Hyder N, Batool K, Walji S, Abbas MK (2025) Advancements in deep learning for early diagnosis of alzheimer\u2019s disease using multimodal neuroimaging: challenges and future directions. Front Neuroinform 19:1557177","journal-title":"Front Neuroinform"},{"key":"291_CR110","doi-asserted-by":"crossref","unstructured":"Jandoubi B, Akhloufi MA (2025) Multimodal artificial intelligence in medical diagnostics. Information","DOI":"10.3390\/info16070591"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-025-00291-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-025-00291-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-025-00291-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:02:59Z","timestamp":1770292979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40708-025-00291-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":110,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["291"],"URL":"https:\/\/doi.org\/10.1186\/s40708-025-00291-w","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,2]]},"assertion":[{"value":"5 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work is based on secondary datasets available online. Hence ethical approval was not necessary.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have seen and approved the current version of the paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"5"}}