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Cited by 68Explainable Artificial Intelligence (XAI)Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare and Education

Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease

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Taiyeb Khosroshahi, Mahdieh, Morsali, Soroush, Gharakhanlou, Sohrab, Motamedi, Alireza, Hassanbaghlou, Saeid, Vahedi, Hadi, Pedrammehr, Siamak, Kabir, Hussain Mohammed Dipu +1 more·2025·Diagnostics
DOI 10.3390/diagnostics15050612PMC PMC11899653
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Abstract

Alzheimer’s disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities, including MRI and PET. Additionally, we discuss current challenges, including dataset limitations, regulatory concerns, and standardization issues, and propose future research directions to improve XAI’s integration into clinical practice. By bridging the gap between AI and clinical interpretability, XAI holds the potential to refine AD diagnostics, personalize treatment strategies, and advance neuroimaging-based research.

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  • Comparative studies have shown inconsistent outputs across XAI methods, highlighting the need for application-specific validation.

    “Although explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations, and layer-wise relevance propagation are gaining traction, comparative studies have shown inconsistent outputs across methods, highlighting the need for application-specific validation282.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [282]

    The Discussion (section 8; page unavailable) says XAI methods have different strengths and that choosing one depends on the data modality and intended use, supporting application-specific selection. The Limitations section (section 9; page unavailable) reports variability arising from imaging modalities and protocols, but our copy does not say comparative studies found inconsistent outputs across XAI methods. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • CNNs applied to PET scans may achieve high area-under-the-curve scores, but their saliency maps frequently fail to align with known pathological regions, reducing clinician trust.

    “Convolutional neural networks applied to PET scans may achieve high area-under-the-curve scores, but their saliency maps frequently fail to align with known pathological regions, reducing clinician trust.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [282]

    Section 6.4 describes an explainable CNN applied to FDG-PET images using saliency maps, and the Discussion says visualization is important for the clinical trustworthiness of CNN-based systems. The cited passages do not report high AUC scores or say that saliency maps frequently miss known pathological regions and thereby reduce clinician trust (no page numbers provided). (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Benchmarking studies indicate that outputs of XAI techniques can be inconsistent and model-dependent.

    “Techniques such as attention layers, SHAP, and patch-based visualizations aim to localize voxel- or gene-level contribution, yet benchmarking studies indicate that their outputs can be inconsistent and model-dependent256,282,289.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [282]

    Section 6.1 says LIME can produce different explanations on repeated runs, and Section 8 says SHAP gives more consistent explanations than LIME. Section 5 distinguishes model-specific from model-agnostic methods. These passages support variability and differences between techniques, but they do not report benchmarking studies showing that XAI outputs are model-dependent. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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  1. [1]Rauniyar K. Thakur S. Nevatia A. Shambharkar P.G. Early Detection of Alzheimer’s Disease: The Importance of Speech Analysis Proceedings of the 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) Salem, India 4–6 May 2023 1069 1073
  2. [2]Gharaibeh M. Elhies M. Almahmoud M. Abualigah S. Elayan O.N. Machine Learning for Alzheimer’s Disease Detection Based on Neuroimaging techniques: A Review Proceedings of the 2022 13th International Conference on Information and Communication Systems (ICICS) 2022 Irbid, Jordan 21–23 June 2022 426 431
  3. [3]Seo Y. Jang H. Lee H. Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease Life 2022 12 275 10.3390/life12020275 35207561 PMC8879055 · DOI
  4. [4]Bevilacqua R. Barbarossa F. Fantechi L. Fornarelli D. Paci E. Bolognini S. Giammarchi C. Lattanzio F. Paciaroni L. Riccardi G.R. Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field J. Clin. Med. 2023 12 5432 10.3390/jcm12165432 37629474 PMC10455452 · DOI
  5. [5]Ashayeri H. Jafarizadeh A. Yousefi M. Farhadi F. Javadzadeh A. Retinal imaging and Alzheimer’s disease: A future powered by Artificial Intelligence Graefe’s Arch. Clin. Exp. Ophthalmol. 2024 262 2389 2401 10.1007/s00417-024-06394-0 38358524 · DOI
  6. [6]Maleki S.F. Yousefi M. Sobhi N. Jafarizadeh A. Alizadehsani R. Gorriz-Saez J.M. Artificial Intelligence in Eye Movements Analysis for Alzheimer’s Disease Early Diagnosis Curr. Alzheimer Res. 2024 21 155 165 10.2174/0115672050322607240529075641 38840390 · DOI
  7. [7]Stumpo V. Kernbach J.M. van Niftrik C.H.B. Sebök M. Fierstra J. Regli L. Serra C. Staartjes V.E. Machine Learning Algorithms in Neuroimaging: An Overview Acta Neurochir. Suppl. 2022 134 125 138 10.1007/978-3-030-85292-4_17 34862537 · DOI
  8. [8]Bhatti U.A. Tang H. Wu G. Marjan S. Hussain A. Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence Int. J. Intell. Syst. 2023 2023 8342104 10.1155/2023/8342104 · DOI
  9. [9]Yao Z. Wang H. Yan W. Wang Z. Zhang W. Wang Z. Zhang G. Artificial intelligence-based diagnosis of Alzheimer’s disease with brain MRI images Eur. J. Radiol. 2023 165 110934 10.1016/j.ejrad.2023.110934 37354773 · DOI
  10. [10]Amoroso N. Quarto S. La Rocca M. Tangaro S. Monaco A. Bellotti R. An explainability artificial intelligence approach to brain connectivity in Alzheimer’s disease Front. Aging Neurosci. 2023 15 1238065 10.3389/fnagi.2023.1238065 37719873 PMC10501457 · DOI
  11. [11]Leandrou S. Lamnisos D. Bougias H. Stogiannos N. Georgiadou E. Achilleos K. Pattichis C.S. Initiative A.s.D.N. A cross-sectional study of explainable machine learning in Alzheimer’s disease: Diagnostic classification using MR radiomic features Front. Aging Neurosci. 2023 15 1149871 10.3389/fnagi.2023.1149871 37358951 PMC10285704 · DOI
  12. [12]Bogdanovic B. Eftimov T. Simjanoska M. In-depth insights into Alzheimer’s disease by using explainable machine learning approach Sci. Rep. 2022 12 6508 10.1038/s41598-022-10202-2 35444165 PMC9021280 · DOI
  13. [13]De Santi L.A. Pasini E. Santarelli M.F. Genovesi D. Positano V. An explainable convolutional neural network for the early diagnosis of Alzheimer’s disease from 18F-FDG PET J. Digit. Imaging 2023 36 189 203 10.1007/s10278-022-00719-3 36344633 PMC9984631 · DOI
  14. [14]Hernandez M. Ramon-Julvez U. Ferraz F. with the ADNI Consortium Explainable AI toward understanding the performance of the top three TADPOLE Challenge methods in the forecast of Alzheimer’s disease diagnosis PLoS ONE 2022 17 e0264695 10.1371/journal.pone.0264695 35522653 PMC9075665 · DOI
  15. [15]Jahan S. Abu Taher K. Kaiser M.S. Mahmud M. Rahman M.S. Hosen A.S. Ra I.-H. Explainable AI-based Alzheimer’s prediction and management using multimodal data PLoS ONE 2023 18 e0294253 10.1371/journal.pone.0294253 37972072 PMC10653516 · DOI · Paper page
  16. [16]Zhang X. Han L. Zhu W. Sun L. Zhang D. An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI IEEE J. Biomed. Health Inform. 2021 26 5289 5297 10.1109/JBHI.2021.3066832 33735087 · DOI
  17. [17]Sarica A. Pelagi A. Aracri F. Arcuri F. Quattrone A. Quattrone A. Initiative A.s.D.N. Sex differences in conversion risk from mild cognitive impairment to Alzheimer’s disease: An explainable machine learning study with random survival forests and SHAP Brain Sci. 2024 14 201 10.3390/brainsci14030201 38539590 PMC10968106 · DOI
  18. [18]Song B. Yoshida S. Initiative A.s.D.N. Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer’s disease classification based on gradient-weighted class activation mapping PLoS ONE 2024 19 e0303278 10.1371/journal.pone.0303278 38771733 PMC11108152 · DOI
  19. [19]Vetrithangam D. Arunadevi B. Pegada N.K. Mehta A. Kumar P. Parihar P. Selvakumar S. Towards Explainable Detection of Alzheimer’s Disease: A Fusion of Deep Convolutional Neural Network and Enhanced Weighted Fuzzy C-Mean Curr. Med. Imaging 2024 20 e15734056317205 10.2174/0115734056317205241014060633 39629569 · DOI
  20. [20]Coluzzi D. Bordin V. Rivolta M.W. Fortel I. Zhan L. Leow A. Baselli G. Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification Bioengineering 2025 12 82 10.3390/bioengineering12010082 39851356 PMC11763248 · DOI
  21. [21]García-Gutiérrez F. Hernández-Lorenzo L. Cabrera-Martín M.N. Matias-Guiu J.A. Ayala J.L. Initiative A.s.D.N. Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI NeuroImage 2024 297 120695 10.1016/j.neuroimage.2024.120695 38942101 · DOI
  22. [22]Mahmud T. Barua K. Habiba S.U. Sharmen N. Hossain M.S. Andersson K. An explainable AI paradigm for Alzheimer’s diagnosis using deep transfer learning Diagnostics 2024 14 345 10.3390/diagnostics14030345 38337861 PMC10855149 · DOI
  23. [23]Cai J. Hu W. Ma J. Si A. Chen S. Gong L. Zhang Y. Yan H. Chen F. Initiative A.s.D.N. Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Utilizing Multi-Modalities Data Brain Sci. 2023 13 1535 10.3390/brainsci13111535 38002495 PMC10670176 · DOI
  24. [24]Brusini L. Cruciani F. Dall’Aglio G. Zajac T. Galazzo I.B. Zucchelli M. Menegaz G. XAI-Based Assessment of the AMURA Model for Detecting Amyloid–β and Tau Microstructural Signatures in Alzheimer’s Disease IEEE J. Transl. Eng. Health Med. 2024 12 569 579 10.1109/JTEHM.2024.3430035 39155922 PMC11329216 · DOI
  25. [25]Saoud L.S. AlMarzouqi H. Explainable early detection of Alzheimer’s disease using ROIs and an ensemble of 138 3D vision transformers Sci. Rep. 2024 14 27756 10.1038/s41598-024-76313-0 39532960 PMC11557913 · DOI
  26. [26]AbdelAziz N.M. Said W. AbdelHafeez M.M. Ali A.H. Advanced interpretable diagnosis of Alzheimer’s disease using SECNN-RF framework with explainable AI Front. Artif. Intell. 2024 7 1456069 10.3389/frai.2024.1456069 39286548 PMC11402894 · DOI
  27. [27]Bhattarai P. Thakuri D.S. Nie Y. Chand G.B. Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition Eur. J. Radiol. 2024 174 111403 10.1016/j.ejrad.2024.111403 38452732 PMC11157778 · DOI
  28. [28]Bapat R. Ma D. Duong T.Q. Predicting Four-Year’s Alzheimer’s Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks J. Alzheimer’s Dis. 2024 97 459 469 10.3233/JAD-230893 38143361 PMC13071850 · DOI
  29. [29]Kim S.Y. Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs Bioengineering 2023 10 701 10.3390/bioengineering10060701 37370632 PMC10295378 · DOI
  30. [30]De Francesco S. Crema C. Archetti D. Muscio C. Reid R.I. Nigri A. Bruzzone M.G. Tagliavini F. Lodi R. D’Angelo E. Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA Sci. Rep. 2023 13 17355 10.1038/s41598-023-43706-6 37833302 PMC10575864 · DOI
  31. [31]Khan S.U. Albanyan A. Bilal M. Ullah S. A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer’s diseases severity prediction J. Alzheimer’s Dis. 2024 102 129 142 10.1177/13872877241283684 39497314 · DOI
  32. [32]Khan Z.A. Waqar M. Chaudhary N.I. Raja M.J.A.A. Khan S. Khan F.A. Chaudhary I.I. Raja M.A.Z. Fractional gradient optimized explainable convolutional neural network for Alzheimer’s disease diagnosis Heliyon 2024 10 e39037 10.1016/j.heliyon.2024.e39037 39498007 PMC11532259 · DOI
  33. [33]Adarsh V. Gangadharan G. Fiore U. Zanetti P. Multimodal classification of Alzheimer’s disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis Sci. Rep. 2024 14 1774 10.1038/s41598-024-52185-2 38245656 PMC10799876 · DOI
  34. [34]Zhang X.X. Tian Y. Wang Z.T. Ma Y.H. Tan L. Yu J.T. The Epidemiology of Alzheimer’s Disease Modifiable Risk Factors and Prevention J. Prev. Alzheimers Dis. 2021 8 313 321 10.14283/jpad.2021.15 34101789 · DOI · Paper page
  35. [35]García-Carlos C.A. Basurto-Islas G. Perry G. Mondragón-Rodríguez S. Meta-Analysis in Transgenic Alzheimer’s Disease Mouse Models Reveals Opposite Brain Network Effects of Amyloid-β and Phosphorylated Tau Proteins J. Alzheimer’s Dis. 2024 99 595 607 10.3233/JAD-231365 38669540 · DOI
  36. [36]Safiri S. Ghaffari Jolfayi A. Fazlollahi A. Morsali S. Sarkesh A. Daei Sorkhabi A. Golabi B. Aletaha R. Motlagh Asghari K. Hamidi S. Alzheimer’s disease: A comprehensive review of epidemiology, risk factors, symptoms diagnosis, management, caregiving, advanced treatments and associated challenges Front. Med. 2024 11 1474043 10.3389/fmed.2024.1474043 PMC11682909 39736972 · DOI
  37. [37]Javaid F.Z. Brenton J. Guo L. Cordeiro M.F. Visual and Ocular Manifestations of Alzheimer’s Disease and Their Use as Biomarkers for Diagnosis and Progression Front. Neurol. 2016 7 55 10.3389/fneur.2016.00055 27148157 PMC4836138 · DOI
  38. [38]Vij R. Arora S. A systematic survey of advances in retinal imaging modalities for Alzheimer’s disease diagnosis Metab. Brain Dis. 2022 37 2213 2243 10.1007/s11011-022-00927-4 35290546 · DOI
  39. [40]Sperling R.A. Bates J. Chua E. Cocchiarella A. Rentz D. Rosen B. Schacter D. Albert M. fMRI studies of associative encoding in young and elderly controls and mild Alzheimer’s disease J. Neurol. Neurosurg. Psychiatry 2003 74 44 50 10.1136/jnnp.74.1.44 12486265 PMC1738201 · DOI
  40. [41]Henriques A.D. Benedet A.L. Camargos E.F. Rosa-Neto P. Nóbrega O.T. Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to Exp. Gerontol. 2018 107 169 177 10.1016/j.exger.2018.01.002 29307736 PMC5951730 · DOI
  41. [43]Frisoni G.B. Fox N.C. Jack Jr C.R. Scheltens P. Thompson P.M. The clinical use of structural MRI in Alzheimer disease Nat. Rev. Neurol. 2010 6 67 77 10.1038/nrneurol.2009.215 20139996 PMC2938772 · DOI · Paper page
  42. [44]Ritchie C. Smailagic N. Noel-Storr A.H. Takwoingi Y. Flicker L. Mason S.E. McShane R. Plasma and cerebrospinal fluid amyloid beta for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI) Cochrane Database Syst. Rev. 2014 2014 CD008782 10.1002/14651858.CD008782.pub4 24913723 PMC6465069 · DOI
  43. [45]Cash D.M. Rohrer J.D. Ryan N.S. Ourselin S. Fox N.C. Imaging endpoints for clinical trials in Alzheimer’s disease Alzheimer’s Res. Ther. 2014 6 87 10.1186/s13195-014-0087-9 25621018 PMC4304258 · DOI
  44. [46]Damoiseaux J.S. Resting-state fMRI as a biomarker for Alzheimer’s disease? Alzheimer’s Res. Ther. 2012 4 8 10.1186/alzrt106 22423634 PMC3334541 · DOI
  45. [47]Rocchi L. Niccolini F. Politis M. Recent imaging advances in neurology J. Neurol. 2015 262 2182 2194 10.1007/s00415-015-7711-x 25808503 · DOI
  46. [48]Clément F. Belleville S. Test–retest reliability of fMRI verbal episodic memory paradigms in healthy older adults and in persons with mild cognitive impairment Hum. Brain Mapp. 2009 30 4033 4047 10.1002/hbm.20827 19492301 PMC6871015 · DOI
  47. [49]Nordberg A. Rinne J.O. Kadir A. Långström B. The use of PET in Alzheimer disease Nat. Rev. Neurol. 2010 6 78 87 10.1038/nrneurol.2009.217 20139997 · DOI
  48. [50]Bollack A. Pemberton H.G. Collij L.E. Markiewicz P. Cash D.M. Farrar G. Barkhof F. Longitudinal amyloid and tau PET imaging in Alzheimer’s disease: A systematic review of methodologies and factors affecting quantification Alzheimers Dement. 2023 19 5232 5252 10.1002/alz.13158 37303269 · DOI
  49. [51]Teng E. Manser P.T. Sanabria Bohorquez S. Wildsmith K.R. Pickthorn K. Baker S.L. Ward M. Kerchner G.A. Weimer R.M. Baseline [(18)F]GTP1 tau PET imaging is associated with subsequent cognitive decline in Alzheimer’s disease Alzheimers Res. Ther. 2021 13 196 10.1186/s13195-021-00937-x 34852837 PMC8638526 · DOI
  50. [52]Lin H.C. Lin K.J. Huang K.L. Chen S.H. Ho T.Y. Huang C.C. Hsu J.L. Chang C.C. Hsiao I.T. Visual reading for [(18)F]Florzolotau ([(18)F]APN-1607) tau PET imaging in clinical assessment of Alzheimer’s disease Front. Neurosci. 2023 17 1148054 10.3389/fnins.2023.1148054 37250400 PMC10213356 · DOI
  51. [53]Lagarde J. Olivieri P. Caillé F. Gervais P. Baron J.C. Bottlaender M. Sarazin M. [(18)F]-AV-1451 tau PET imaging in Alzheimer’s disease and suspected non-AD tauopathies using a late acquisition time window J. Neurol. 2019 266 3087 3097 10.1007/s00415-019-09530-7 31535269 · DOI
  52. [54]Josephs K.A. Whitwell J.L. Ahmed Z. Shiung M.M. Weigand S.D. Knopman D.S. Boeve B.F. Parisi J.E. Petersen R.C. Dickson D.W. β-amyloid burden is not associated with rates of brain atrophy Ann. Neurol. 2008 63 204 212 10.1002/ana.21223 17894374 PMC2735194 · DOI
  53. [55]Ridha B.H. Anderson V.M. Barnes J. Boyes R.G. Price S.L. Rossor M.N. Whitwell J.L. Jenkins L. Black R.S. Grundman M. Volumetric MRI and cognitive measures in Alzheimer disease: Comparison of markers of progression J. Neurol. 2008 255 567 574 10.1007/s00415-008-0750-9 18274807 · DOI
  54. [56]Jack Jr C. Slomkowski M. Gracon S. Hoover T. Felmlee J. Stewart K. Xu Y. Shiung M. O’brien P. Cha R. MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD Neurology 2003 60 253 260 10.1212/01.WNL.0000042480.86872.03 12552040 PMC2745302 · DOI
  55. [57]Gaubert M. Lange C. Garnier-Crussard A. Köbe T. Bougacha S. Gonneaud J. de Flores R. Tomadesso C. Mézenge F. Landeau B. Topographic patterns of white matter hyperintensities are associated with multimodal neuroimaging biomarkers of Alzheimer’s disease Alzheimers Res. Ther. 2021 13 29 10.1186/s13195-020-00759-3 33461618 PMC7814451 · DOI
  56. [58]Harrison T.M. Landau S.M. Baker S.L. Lee J. Ward T.J. Murphy A. Chadwick T. Mormino E.C. Johnson S.C. Rogalski E.J. Harmonization of amyloid and tau PET tracers in a multi-center cohort from NIA Alzheimer’s disease research centers in the United States Alzheimer’s Dement. 2023 19 e079015 10.1002/alz.079015 · DOI
  57. [59]Okamura N. Harada R. Furumoto S. Arai H. Yanai K. Kudo Y. Tau PET imaging in Alzheimer’s disease Curr. Neurol. Neurosci. Rep. 2014 14 500 10.1007/s11910-014-0500-6 25239654 · DOI
  58. [60]Lagarde J. Olivieri P. Tonietto M. Rodrigo S. Gervais P. Caillé F. Moussion M. Bottlaender M. Sarazin M. Could tau-PET imaging contribute to a better understanding of the different patterns of clinical progression in Alzheimer’s disease? A 2-year longitudinal study Alzheimers Res. Ther. 2023 15 91 10.1186/s13195-023-01237-2 37138309 PMC10155356 · DOI
  59. [61]Pan Y. Liu M. Lian C. Zhou T. Xia Y. Shen D. Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer’s Disease Diagnosis Med. Image Comput. Comput. Assist. Interv. 2018 11072 455 463 10.1007/978-3-030-00931-1_52 34355223 PMC8336606 · DOI
  60. [62]Zhang J. He X. Qing L. Gao F. Wang B. BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer’s disease diagnosis Comput. Methods Programs Biomed. 2022 217 106676 10.1016/j.cmpb.2022.106676 35167997 · DOI
  61. [63]Wahler F. Neubert M. A scientific definition of explainable artificial intelligence for decision making Int. J. Teach. Case Stud. 2023 14 88 116 10.1504/IJTCS.2023.131664 · DOI
  62. [66]Mankodiya H. Jadav D. Gupta R. Tanwar S. Alharbi A. Tolba A. Neagu B.-C. Raboaca M.S. Xai-fall: Explainable ai for fall detection on wearable devices using sequence models and xai techniques Mathematics 2022 10 1990 10.3390/math10121990 · DOI
  63. [67]Sekaran K. Alsamman A.M. George Priya Doss C. Zayed H. Bioinformatics investigation on blood-based gene expressions of Alzheimer’s disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach Metab. Brain Dis. 2023 38 1297 1310 10.1007/s11011-023-01171-0 36809524 PMC9942063 · DOI
  64. [68]Klepl D. He F. Wu M. Blackburn D.J. Sarrigiannis P. Adaptive gated graph convolutional network for explainable diagnosis of Alzheimer’s disease using EEG data IEEE Trans. Neural Syst. Rehabil. Eng. 2023 31 3978 3987 10.1109/TNSRE.2023.3321634 37792656 · DOI
  65. [69]Morabito F.C. Ieracitano C. Mammone N. An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing Clin. EEG Neurosci. 2023 54 51 60 10.1177/15500594211063662 34889152 · DOI
  66. [72]Holzinger A. Langs G. Denk H. Zatloukal K. Müller H. Causability and explainability of artificial intelligence in medicine Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2019 9 e1312 10.1002/widm.1312 32089788 PMC7017860 · DOI · Paper page
  67. [74]Molnar C. Casalicchio G. Bischl B. Interpretable machine learning–a brief history, state-of-the-art and challenges Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases Ghent, Belgium 14–18 September 2020 417 431
  68. [75]Gilpin L.H. Bau D. Yuan B.Z. Bajwa A. Specter M. Kagal L. Explaining explanations: An overview of interpretability of machine learning Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Turin, Italy 1–3 October 2018 80 89
  69. [78]Tonekaboni S. Joshi S. McCradden M.D. Goldenberg A. What clinicians want: Contextualizing explainable machine learning for clinical end use Proceedings of the Machine Learning for Healthcare Conference Ann Arbor, MI, USA 8–10 August 2019 359 380
  70. [80]Abrantes J. Rouzrokh P. Explaining explainability: The role of XAI in medical imaging Eur. J. Radiol. 2024 173 111389 10.1016/j.ejrad.2024.111389 38422609 · DOI
  71. [81]Bordin V. Coluzzi D. Rivolta M.W. Baselli G. Explainable AI Points to White Matter Hyperintensities for Alzheimer’s Disease Identification: A Preliminary Study Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Glasgow, UK 11–15 July 2022 Volume 2022 484 487 10.1109/embc48229.2022.9871306 36086369 · DOI
  72. [82]Tekkesinoglu S. Pudas S. Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer’s disease classification Front. Artif. Intell. 2023 6 1334613 10.3389/frai.2023.1334613 38259822 PMC10801225 · DOI
  73. [83]Miró-Nicolau M. Jaume-i-Capó A. Moyà-Alcover G. Assessing fidelity in xai post-hoc techniques: A comparative study with ground truth explanations datasets Artif. Intell. 2024 335 104179 10.1016/j.artint.2024.104179 · DOI
  74. [84]Holzinger A. Saranti A. Molnar C. Biecek P. Samek W. Explainable AI methods-a brief overview Proceedings of the International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers Vienna, Austria 18 July 2020 13 38
  75. [85]Nguyen H.T.T. Cao H.Q. Nguyen K.V.T. Pham N.D.K. Evaluation of explainable artificial intelligence: Shap, lime, and cam Proceedings of the FPT AI Conference Online 17–21 August 2021 1 6
  76. [86]Salih A.M. Raisi-Estabragh Z. Galazzo I.B. Radeva P. Petersen S.E. Lekadir K. Menegaz G. A perspective on explainable artificial intelligence methods: Shap and lime Adv. Intell. Syst. 2024 2400304 10.1002/aisy.202400304 · DOI · Paper page
  77. [87]Ullah N. Hassan M. Khan J.A. Anwar M.S. Aurangzeb K. Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images Int. J. Imaging Syst. Technol. 2024 34 e23012 10.1002/ima.23012 · DOI
  78. [88]de Wolf T.H. Snelders M. Essers J. Smal I. CA-LIME: An Algorithm that Explains Classification Results of Cardiomyocyte Beating Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) Cartagena, Colombia 18–21 April 2023 1 4
  79. [89]Gong H. Wang M. Zhang H. Elahe M.F. Jin M. An explainable AI approach for the rapid diagnosis of COVID-19 using ensemble learning algorithms Front. Public Health 2022 10 874455 10.3389/fpubh.2022.874455 35801239 PMC9253566 · DOI
  80. [90]Kamal M.S. Northcote A. Chowdhury L. Dey N. Crespo R.G. Herrera-Viedma E. Alzheimer’s patient analysis using image and gene expression data and explainable-AI to present associated genes IEEE Trans. Instrum. Meas. 2021 70 1 7 10.1109/TIM.2021.3107056 · DOI
  81. [91]Duamwan L.M. Bird J.J. Explainable AI for Medical Image Processing: A Study on MRI in Alzheimer’s Disease Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments Corfu, Greece 5–7 July 2023 480 484
  82. [92]Rashmi U. Singh T. Ambesange S. MRI image based Ensemble Voting Classifier for Alzheimer’s Disease Classification with Explainable AI Technique Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Lonavla, India 7–9 April 2023 1 6
  83. [93]Shad H.A. Rahman Q.A. Asad N.B. Bakshi A.Z. Mursalin S.F. Reza M.T. Parvez M.Z. Exploring Alzheimer’s disease prediction with XAI in various neural network models Proceedings of the TENCON 2021-2021 IEEE Region 10 Conference (TENCON) Auckland, New Zealand 7–10 December 2021 720 725
  84. [94]Liu F. Evaluating the Impact of Data Augmentation on Explainable AI in Medical Image Analysis Ph.D. Thesis Eindhoven University of Technology Eindhoven Eindhoven, The Netherlands 2024
  85. [95]Bhattacharjee S. Hwang Y.-B. Ikromjanov K. Sumon R.I. Kim H.-C. Choi H.-K. An explainable computer vision in histopathology: Techniques for interpreting black box model Proceedings of the 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Jeju, Republic of Korea 21–24 February 2022 392 398
  86. [96]Gaur L. Bhandari M. Razdan T. Mallik S. Zhao Z. Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data Front. Genet. 2022 13 822666 10.3389/fgene.2022.822666 35360838 PMC8964286 · DOI
  87. [97]Ahmed S. Nobel S.N. Ullah O. An effective deep cnn model for multiclass brain tumor detection using mri images and shap explainability Proceedings of the 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Chittagong, Bangladesh 23–25 February 2023 1 6
  88. [98]Raptis S. Ilioudis C. Theodorou K. From pixels to prognosis: Unveiling radiomics models with SHAP and LIME for enhanced interpretability Biomed. Phys. Eng. Express 2024 10 035016 10.1088/2057-1976/ad34db 38498925 · DOI
  89. [99]Shi Y. Zou Y. Liu J. Wang Y. Chen Y. Sun F. Yang Z. Cui G. Zhu X. Cui X. Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP Front. Oncol. 2022 12 897596 10.3389/fonc.2022.897596 36091102 PMC9458917 · DOI
  90. [102]Lombardi A. Diacono D. Amoroso N. Biecek P. Monaco A. Bellantuono L. Pantaleo E. Logroscino G. De Blasi R. Tangaro S. A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer’s Disease Brain Inform. 2022 9 17 10.1186/s40708-022-00165-5 35882684 PMC9325942 · DOI
  91. [103]Chun M.Y. Park C.J. Kim J. Jeong J.H. Jang H. Kim K. Seo S.W. Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment Front. Aging Neurosci. 2022 14 898940 10.3389/fnagi.2022.898940 35992586 PMC9389270 · DOI
  92. [104]Zhang X. Han L. Han L. Chen H. Dancey D. Zhang D. sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer’s disease diagnosis and discriminative atrophy localisation with Structural MRI arXiv 2023 2302.08967
  93. [105]Xu X. Yan X. A Convenient and Reliable Multi-Class Classification Model based on Explainable Artificial Intelligence for Alzheimer’s Disease Proceedings of the 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) Dalian, China 20–21 August 2022 671 675
  94. [106]Ekuma G. Hier D.B. Obafemi-Ajayi T. An Explainable Deep Learning Model for Prediction of Severity of Alzheimer’s Disease Proceedings of the 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Eindhoven, The Netherlands 29–31 August 2023 1 8
  95. [107]Yilmaz D. Development and Evaluation of an Explainable Diagnostic AI for Alzheimer’s Disease Proceedings of the 2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS) Galala, Egypt 3–5 September 2023 1 6
  96. [108]Rahim N. El-Sappagh S. Ali S. Muhammad K. Del Ser J. Abuhmed T. Prediction of Alzheimer’s progression based on multimodal deep-learning-based fusion and visual explainability of time-series data Inf. Fusion 2023 92 363 388 10.1016/j.inffus.2022.11.028 · DOI
  97. [109]Yi F. Yang H. Chen D. Qin Y. Han H. Cui J. Bai W. Ma Y. Zhang R. Yu H. XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease BMC Med. Inform. Decis. Mak. 2023 23 137 10.1186/s12911-023-02238-9 37491248 PMC10369804 · DOI
  98. [110]Böhle M. Eitel F. Weygandt M. Ritter K. Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer’s disease classification Front. Aging Neurosci. 2019 11 194 10.3389/fnagi.2019.00194 31417397 PMC6685087 · DOI · Paper page
  99. [111]Grigorescu I. Cordero-Grande L. Edwards A.D. Hajnal J. Modat M. Deprez M. Interpretable convolutional neural networks for preterm birth classification arXiv 2019 1910.00071
  100. [112]Sadafi A. Adonkina O. Khakzar A. Lienemann P. Hehr R.M. Rueckert D. Navab N. Marr C. Pixel-level explanation of multiple instance learning models in biomedical single cell images Proceedings of the International Conference on Information Processing in Medical Imaging Bariloche, Argentina 18–23 June 2023 170 182
  101. [113]Pohl T. Jakab M. Benesova W. Interpretability of deep neural networks used for the diagnosis of Alzheimer’s disease Int. J. Imaging Syst. Technol. 2022 32 673 686 10.1002/ima.22657 · DOI
  102. [114]Sudar K.M. Nagaraj P. Nithisaa S. Aishwarya R. Aakash M. Lakshmi S.I. Alzheimer’s Disease Analysis using Explainable Artificial Intelligence (XAI) Proceedings of the 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Erode, India 7–9 April 2022 419 423
  103. [115]Leonardsen E.H. Persson K. Grødem E. Dinsdale N. Schellhorn T. Roe J.M. Vidal-Piñeiro D. Sørensen Ø. Kaufmann T. Westman E. Characterizing personalized neuropathology in dementia and mild cognitive impairment with explainable artificial intelligence medRxiv 2023 10.1101/2023.06.22.23291592 · DOI
  104. [116]Mohamed E. Sirlantzis K. Howells G. A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation Displays 2022 73 102239 10.1016/j.displa.2022.102239 · DOI · Paper page
  105. [117]Zhang Y. Hong D. McClement D. Oladosu O. Pridham G. Slaney G. Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging J. Neurosci. Methods 2021 353 109098 10.1016/j.jneumeth.2021.109098 33582174 · DOI
  106. [118]Chethana S. Charan S.S. Srihitha V. Palaniswamy S. Pati P.B. A Novel Approach for Alzheimer’s Disease Detection using XAI and Grad-CAM Proceedings of the 2023 4th IEEE Global Conference for Advancement in Technology (GCAT) Bangalore, India 6–8 October 2023 1 6
  107. [119]Reza M.S. Kabir M.M.J. Mollah M.A.R. Nahin N.I. Exploring Deep Convolutional Neural Networks: A Grad-CAM Enhanced Comparative Study for Automated COVID-19 Diagnosis from Chest X-Ray Images Proceedings of the 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) Bangalore, India 29–31 December 2023 1 6
  108. [120]M M.M. T.R M. V V.K. Guluwadi S. Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50 BMC Med. Imaging 2024 24 107 10.1186/s12880-024-01292-7 38734629 PMC11088067 · DOI
  109. [121]Ruengchaijatuporn N. Chatnuntawech I. Teerapittayanon S. Sriswasdi S. Itthipuripat S. Hemrungrojn S. Bunyabukkana P. Petchlorlian A. Chunamchai S. Chotibut T. An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks Alzheimer’s Res. Ther. 2022 14 111 10.1186/s13195-022-01043-2 35945568 PMC9361513 · DOI
  110. [122]Jain V. Nankar O. Jerrish D.J. Gite S. Patil S. Kotecha K. A novel AI-based system for detection and severity prediction of dementia using MRI IEEE Access 2021 9 154324 154346 10.1109/ACCESS.2021.3127394 · DOI
  111. [123]Oh K. Yoon J.S. Suk H.-I. Learn-explain-reinforce: Counterfactual reasoning and its guidance to reinforce an Alzheimer’s Disease diagnosis model IEEE Trans. Pattern Anal. Mach. Intell. 2022 45 4843 4857 10.1109/TPAMI.2022.3197845 35947563 · DOI
  112. [124]Kim M. Kim J. Qu J. Huang H. Long Q. Sohn K.-A. Kim D. Shen L. Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Houston, TX, USA 9–12 December 2021 1381 1384 PMC8922159 35299717
  113. [125]Aminu M. Ahmad N.A. Noor M.H.M. Covid-19 detection via deep neural network and occlusion sensitivity maps Alex. Eng. J. 2021 60 4829 4855 10.1016/j.aej.2021.03.052 · DOI
  114. [126]Rieke J. Eitel F. Weygandt M. Haynes J.-D. Ritter K. Visualizing convolutional networks for MRI-based diagnosis of Alzheimer’s disease Proceedings of the Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018 Granada, Spain 16–20 September 2018 Proceedings 1 24 31
  115. [127]Farahani F.V. Fiok K. Lahijanian B. Karwowski W. Douglas P.K. Explainable AI: A review of applications to neuroimaging data Front. Neurosci. 2022 16 906290 10.3389/fnins.2022.906290 36583102 PMC9793854 · DOI
  116. [128]Han X. Kwitt R. Aylward S. Bakas S. Menze B. Asturias A. Vespa P. Van Horn J. Niethammer M. Brain extraction from normal and pathological images: A joint PCA/image-reconstruction approach NeuroImage 2018 176 431 445 10.1016/j.neuroimage.2018.04.073 29730494 PMC6036616 · DOI
  117. [129]Prado P. Medel V. Gonzalez-Gomez R. Sainz-Ballesteros A. Vidal V. Santamaría-García H. Moguilner S. Mejia J. Slachevsky A. Behrens M.I. The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds Sci. Data 2023 10 889 10.1038/s41597-023-02806-8 38071313 PMC10710425 · DOI
  118. [130]Jagust W.J. Koeppe R.A. Rabinovici G.D. Villemagne V.L. Harrison T.M. Landau S.M. Initiative A.s.D.N. The ADNI PET Core at 20 Alzheimer’s Dement. 2024 20 7340 7349 10.1002/alz.14165 39108002 PMC11485322 · DOI · Paper page
  119. [131]Wyman B.T. Harvey D.J. Crawford K. Bernstein M.A. Carmichael O. Cole P.E. Crane P.K. DeCarli C. Fox N.C. Gunter J.L. Standardization of analysis sets for reporting results from ADNI MRI data Alzheimers Dement. 2013 9 332 337 10.1016/j.jalz.2012.06.004 23110865 PMC3891834 · DOI
  120. [132]Marcus D.S. Wang T.H. Parker J. Csernansky J.G. Morris J.C. Buckner R.L. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults J. Cogn. Neurosci. 2007 19 1498 1507 10.1162/jocn.2007.19.9.1498 17714011 · DOI · Paper page
  121. [134]Liang W. Tadesse G.A. Ho D. Fei-Fei L. Zaharia M. Zhang C. Zou J. Advances, challenges and opportunities in creating data for trustworthy AI Nat. Mach. Intell. 2022 4 669 677 10.1038/s42256-022-00516-1 · DOI
  122. [135]Hagendorff T. Wezel K. 15 challenges for AI: Or what AI (currently) can’t do Ai Soc. 2020 35 355 365 10.1007/s00146-019-00886-y · DOI · Paper page
  123. [137]Gipiškis R. Tsai C.-W. Kurasova O. Explainable AI (XAI) in image segmentation in medicine, industry, and beyond: A survey ICT Express 2024 10 1331 1354 10.1016/j.icte.2024.09.008 · DOI
  124. [138]Kakogeorgiou I. Karantzalos K. Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing Int. J. Appl. Earth Obs. Geoinf. 2021 103 102520 10.1016/j.jag.2021.102520 · DOI
  125. [139]Vimbi V. Shaffi N. Mahmud M. Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer’s disease detection Brain Inform. 2024 11 10 10.1186/s40708-024-00222-1 38578524 PMC10997568 · DOI
  126. [140]Firth N.C. Primativo S. Marinescu R.-V. Shakespeare T.J. Suarez-Gonzalez A. Lehmann M. Carton A. Ocal D. Pavisic I. Paterson R.W. Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy Brain 2019 142 2082 2095 10.1093/brain/awz136 31219516 PMC6598737 · DOI
  127. [141]van Oostveen W.M. de Lange E.C.M. Imaging Techniques in Alzheimer’s Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring Int. J. Mol. Sci. 2021 22 2110 10.3390/ijms22042110 33672696 PMC7924338 · DOI · Paper page
  128. [142]Johnson K.A. Fox N.C. Sperling R.A. Klunk W.E. Brain imaging in Alzheimer disease Cold Spring Harb. Perspect. Med. 2012 2 a006213 10.1101/cshperspect.a006213 22474610 PMC3312396 · DOI · Paper page
  129. [143]Vanhoutte M. Lopes R. Maureille A. Delmaire C. Hossein-Foucher C. Rollin A. Semah F. Pasquier F. P1-291: Hypometabolism patterns using FDG-PET in typical and atypical sporadic forms of early-onset Alzheimer’s disease Alzheimer’s Dement. 2016 12 P532 10.1016/j.jalz.2016.06.1041 · DOI
  130. [144]Aramadaka S. Mannam R. Narayanan R.S. Bansal A. Yanamaladoddi V.R. Sarvepalli S.S. Vemula S.L. Neuroimaging in Alzheimer’s disease for early diagnosis: A comprehensive review Cureus 2023 15 1 11 10.7759/cureus.38544 PMC10239271 37273363 · DOI
  131. [145]Ferrara E. Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies Sci 2023 6 3 10.3390/sci6010003 · DOI · Paper page
  132. [147]Galazzo I.B. Cruciani F. Brusini L. Salih A.M.A. Radeva P. Storti S.F. Menegaz G. Explainable Artificial Intelligence for Magnetic Resonance Imaging Aging Brainprints: Grounds and challenges IEEE Signal Process. Mag. 2022 39 99 116 10.1109/MSP.2021.3126573 · DOI
  133. [148]Smucny J. Shi G. Davidson I. Deep learning in neuroimaging: Overcoming challenges with emerging approaches Front. Psychiatry 2022 13 912600 10.3389/fpsyt.2022.912600 35722548 PMC9200984 · DOI
  134. [149]Zhao Z. Chuah J.H. Lai K.W. Chow C.O. Gochoo M. Dhanalakshmi S. Wang N. Bao W. Wu X. Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: A review Front. Comput. Neurosci. 2023 17 1038636 10.3389/fncom.2023.1038636 36814932 PMC9939698 · DOI
  135. [150]Mahim S. Ali M.S. Hasan M.O. Nafi A.A.N. Sadat A. Al Hasan S. Shareef B. Ahsan M.M. Islam M.K. Miah M.S. Unlocking the Potential of XAI for Improved Alzheimer’s Disease Detection and Classification Using a ViT-GRU Model IEEE Access 2024 12 8390 8412 10.1109/ACCESS.2024.3351809 · DOI
  136. [152]Keefe S.J. McKay N.S. LaMontagne P.J. Flores S. Gordon B.A. Benzinger T.L. Containerizing neuroimaging workflows for scalable and reproducible analyses Alzheimer’s Dement. 2023 19 e081785 10.1002/alz.081785 · DOI
  137. [153]Kumar S. Payne P. Sotiras A. Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders arXiv 2023 2312.00992 PMC11600985 39610661
  138. [154]Sinha H. Raamana P.R. Solving the pervasive problem of protocol non-compliance in neuroimaging using an open-source tool mrQA Alzheimer’s Dement. 2023 19 e079944 10.1002/alz.079944 PMC11329586 38861098 · DOI
  139. [155]Odusami M. Maskeliūnas R. Damaševičius R. An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging Sensors 2022 22 740 10.3390/s22030740 35161486 PMC8839926 · DOI
  140. [156]Odusami M. Maskeliūnas R. Damaševičius R. Misra S. Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: A systematic review and meta-analysis Cogn. Neurodyn. 2024 18 775 794 10.1007/s11571-023-09993-5 38826669 PMC11143094 · DOI
  141. [157]Chhabra G.S. Guru A. Rajput B.J. Dewangan L. Swarnkar S.K. Multimodal Neuroimaging for Early Alzheimer’s detection: A Deep Learning Approach Proceedings of the 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) Delhi, India 6–8 July 2023 1 5
  142. [158]Jo T. Nho K. Risacher S.L. Saykin A.J. Deep Learning-based Integration of neuroimaging and genetic data for classification of Alzheimer’s Disease Alzheimer’s Dement. 2023 19 e074318 10.1002/alz.074318 · DOI
  143. [159]Aberathne I. Kulasiri D. Samarasinghe S. Detection of Alzheimer’s disease onset using MRI and PET neuroimaging: Longitudinal data analysis and machine learning Neural Regen. Res. 2023 18 2134 2140 10.4103/1673-5374.367840 37056120 PMC10328296 · DOI

Cited by

Sorted by citations

Authors
Taiyeb Khosroshahi, M., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., Pedrammehr, S., Kabir, H. M. D., & Jafarizadeh, A.
Affiliations
  • Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5164736931, Iran
  • Tabriz University of Medical Sciences (IR)
  • Tabriz Islamic Arts University (IR)
  • Charles Sturt University (AU)
Corresponding author
Kabir, Hussain Mohammed Dipu
ORCID
Venue
Diagnostics
Publisher
MDPI AG
Published
2025-03-04
Volume
15
Issue
5
Pages
612
ISSN
2075-4418 (ISSN-L)
DOI
10.3390/diagnostics15050612
PMC
PMC11899653
OpenAlex
W4408155937
Cited by
68
References
16
Citations by year
2026: 40 · 2025: 28
FWCI
6.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Diagnostics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Charles Sturt University Research Output (CRO) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
explainable AI, Alzheimer's disease, neuroimaging, model interpretability, SHAP, lime, Grad-CAM, layer-wise relevance propagation, biomarker discovery, disease progression monitoring, Alzheimer's disease staging, magnetic resonance imaging, PET, clinical translation of AI, regulatory challenges

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