{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:33:47Z","timestamp":1792791227354,"version":"4.5.0"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Deutsches Zentrum f\u00fcr Neurodegenerative Erkrankungen e.V. (DZNE) in der Helmholtz-Gemeinschaft","id":[{"id":"https:\/\/ror.org\/043j0f473","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Explainable Artificial Intelligence (XAI) methods enhance the diagnostic efficiency of clinical decision support systems by making the predictions of a convolutional neural network\u2019s (CNN) on brain imaging more transparent and trustworthy. However, their clinical adoption is limited due to limited validation of the explanation quality. Our study introduces a framework that evaluates XAI methods by integrating neuroanatomical morphological features with CNN-generated relevance maps for disease classification. We trained a CNN using brain MRI scans from six cohorts: ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD (N\u2009=\u20093253), including participants that were cognitively normal, with amnestic mild cognitive impairment, dementia due to Alzheimer\u2019s disease and frontotemporal dementia. Clustering analysis benchmarked different explanation space configurations by using morphological features as proxy-ground truth. We implemented three post-hoc explanations methods: (i) by simplifying model decisions, (ii) explanation-by-example, and (iii) textual explanations. A qualitative evaluation by clinicians (N\u2009=\u20096) was performed to assess their clinical validity. Clustering performance improved in morphology enriched explanation spaces, improving both homogeneity and completeness of the clusters. Post hoc explanations by model simplification largely delineated converters and stable participants, while explanation-by-example presented possible cognition trajectories. Textual explanations gave rule-based summarization of pathological findings. Clinicians\u2019 qualitative evaluation highlighted challenges and opportunities of XAI for different clinical applications. Our study refines XAI explanation spaces and applies various approaches for generating explanations. Within the context of AI-based decision support system in dementia research we found the explanations methods to be promising towards enhancing diagnostic efficiency, backed up by the clinical assessments.<\/jats:p>","DOI":"10.1038\/s41598-025-26227-2","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T09:17:24Z","timestamp":1762939044000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An unsupervised XAI framework for dementia detection with context enrichment"],"prefix":"10.1038","volume":"15","author":[{"given":"Devesh","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuf","family":"Brima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fedor","family":"Levin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjarne","family":"Hiller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Hermann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irene","family":"Villar-Munoz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Beichert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Bernhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharina","family":"Buerger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michaela","family":"Butryn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Dechent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emrah","family":"D\u00fczel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Ewers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Fliessbach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silka D.","family":"Freiesleben","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzel","family":"Glanz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Hetzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Janowitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doreen","family":"G\u00f6r\u00df","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingo","family":"Kilimann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okka","family":"Kimmich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Laske","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Levin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Lohse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Falk","family":"Luesebrink","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Munk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Perneczky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Peters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Preis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Priller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Prudlo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana","family":"Prychynenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris S.","family":"Rauchmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayda","family":"Rostamzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Roy-Kluth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Scheffler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anja","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louise","family":"Droste zu Senden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f6rn H.","family":"Schott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annika","family":"Spottke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthis","family":"Synofzik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Wiltfang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Jessen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc-Andr\u00e9","family":"Weber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan J.","family":"Teipel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Dyrba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"26227_CR1","doi-asserted-by":"publisher","first-page":"e105","DOI":"10.1016\/S2468-2667(21)00249-8","volume":"7","author":"Emma Nichols","year":"2022","unstructured":"Emma Nichols e. a. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. Lancet Public. Health. 7, e105\u2013e125. https:\/\/doi.org\/10.1016\/S2468-2667(21)00249-8 (2022).","journal-title":"Lancet Public. Health"},{"key":"26227_CR2","unstructured":"Alzheimer\u2019s disease international. World Alzheimer Report 2022. Life after diagnosis: Navigating treatment, care and support. (2022)."},{"key":"26227_CR3","doi-asserted-by":"publisher","first-page":"l886","DOI":"10.1136\/bmj.l886","volume":"364","author":"DS Watson","year":"2019","unstructured":"Watson, D. S. et al. Clinical applications of machine learning algorithms: beyond the black box. BMJ (Clinical Res. ed.). 364, l886. https:\/\/doi.org\/10.1136\/bmj.l886 (2019).","journal-title":"BMJ (Clinical Res. ed.)"},{"key":"26227_CR4","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1609\/aimag.v38i3.2741","volume":"38","author":"B Goodman","year":"2017","unstructured":"Goodman, B. & Flaxman, S. European union regulations on algorithmic decision making and a right to explanation. AI Magazine. 38, 50\u201357. https:\/\/doi.org\/10.1609\/aimag.v38i3.2741 (2017).","journal-title":"AI Magazine"},{"key":"26227_CR5","doi-asserted-by":"publisher","first-page":"118888","DOI":"10.1016\/j.eswa.2022.118888","volume":"213","author":"F Cabitza","year":"2023","unstructured":"Cabitza, F. et al. Quod Erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI. Expert Syst. Appl. 213, 118888. https:\/\/doi.org\/10.1016\/j.eswa.2022.118888 (2023).","journal-title":"Expert Syst. Appl."},{"key":"26227_CR6","unstructured":"The Royal Society. Explainable AI: the basics Policy briefing. (2019)."},{"key":"26227_CR7","unstructured":"OECD. The OECD AI Principles. (2024)."},{"key":"26227_CR8","unstructured":"The international research center for ai ethics and governance. International Research Center for AI Ethics and Governance. A Cross Cultural and Transdisciplinary Center for Building Responsible and Beneficial AI for Human and Ecology Good."},{"key":"26227_CR9","doi-asserted-by":"publisher","first-page":"411","DOI":"10.20344\/amp.20469","volume":"37","author":"J Tavares","year":"2024","unstructured":"Tavares, J. Application of artificial intelligence in healthcare: the need for more interpretable artificial intelligence. Acta Med. Port. 37, 411\u2013414. https:\/\/doi.org\/10.20344\/amp.20469 (2024).","journal-title":"Acta Med. Port."},{"key":"26227_CR10","first-page":"103390e23010018","volume":"23","author":"P Linardatos","year":"2020","unstructured":"Linardatos, P., Papastefanopoulos, V. & Kotsiantis, S. Explainable AI: A review of machine learning interpretability methods. Entropy (Basel Switzerland). 23, 103390e23010018 (2020).","journal-title":"Entropy (Basel Switzerland)"},{"key":"26227_CR11","doi-asserted-by":"publisher","first-page":"2135","DOI":"10.1002\/alz.12948","volume":"19","author":"SA Martin","year":"2023","unstructured":"Martin, S. A., Townend, F. J., Barkhof, F. & Cole, J. H. Interpretable machine learning for dementia: A systematic review. Alzheimer\u2019s Dement. J. Alzheimer\u2019s Assoc. 19, 2135\u20132149. https:\/\/doi.org\/10.1002\/alz.12948 (2023).","journal-title":"Alzheimer\u2019s Dement. J. Alzheimer\u2019s Assoc."},{"key":"26227_CR12","doi-asserted-by":"publisher","first-page":"110592","DOI":"10.1016\/j.ejrad.2022.110592","volume":"157","author":"AM Groen","year":"2022","unstructured":"Groen, A. M., Kraan, R., Amirkhan, S. F., Daams, J. G. & Maas, M. A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: limited use of explainable AI? Eur. J. Radiol. 157, 110592. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110592 (2022).","journal-title":"Eur. J. Radiol."},{"key":"26227_CR13","doi-asserted-by":"crossref","unstructured":"Horta, V. A. C. & Mileo, A. Generating local textual explanations for CNNs: A semantic approach based on knowledge graphs. In: S. Bandini, F. Gasparini, V. Mascardi, M. Palmonari & G. Vizzari, (eds).\u00a0AIxIA 2021 \u2013 Advances in Artificial Intelligence.\u00a013196 532\u2013549 (Springer International Publishing, 2022).","DOI":"10.1007\/978-3-031-08421-8_37"},{"key":"26227_CR14","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/info11020122","volume":"11","author":"G Futia","year":"2020","unstructured":"Futia, G. & Vetr\u00f2, A. On the integration of knowledge graphs into deep learning models for a more comprehensible AI\u2014Three challenges for future research. Information 11, 122. https:\/\/doi.org\/10.3390\/info11020122 (2020).","journal-title":"Information"},{"key":"26227_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3563691","volume":"55","author":"R Ibrahim","year":"2023","unstructured":"Ibrahim, R. & Shafiq, M. O. Explainable convolutional neural networks: A Taxonomy, Review, and future directions. ACM Comput. Surv. 55, 1\u201337. https:\/\/doi.org\/10.1145\/3563691 (2023).","journal-title":"ACM Comput. Surv."},{"key":"26227_CR16","doi-asserted-by":"publisher","first-page":"688969","DOI":"10.3389\/fdata.2021.688969","volume":"4","author":"V Belle","year":"2021","unstructured":"Belle, V. & Papantonis, I. Principles and practice of explainable machine learning. Front. Big Data. 4, 688969. https:\/\/doi.org\/10.3389\/fdata.2021.688969 (2021).","journal-title":"Front. Big Data"},{"key":"26227_CR17","doi-asserted-by":"publisher","first-page":"107530","DOI":"10.1016\/j.knosys.2021.107530","volume":"233","author":"EM Kenny","year":"2021","unstructured":"Kenny, E. M. & Keane, M. T. Explaining deep learning using examples: optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI. Knowl. Based Syst. 233, 107530. https:\/\/doi.org\/10.1016\/j.knosys.2021.107530 (2021).","journal-title":"Knowl. Based Syst."},{"key":"26227_CR18","doi-asserted-by":"publisher","first-page":"12900","DOI":"10.1038\/s41598-020-68858-7","volume":"10","author":"MA Schulz","year":"2020","unstructured":"Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D. & Georgatzis, K. Inferring disease subtypes from clusters in explanation space. Sci. Rep. 10, 12900. https:\/\/doi.org\/10.1038\/s41598-020-68858-7 (2020).","journal-title":"Sci. Rep."},{"key":"26227_CR19","doi-asserted-by":"publisher","first-page":"4991","DOI":"10.1007\/s00330-020-07561-7","volume":"31","author":"F Dong","year":"2021","unstructured":"Dong, F. et al. One step further into the blackbox: a pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound. Eur. Radiol. 31, 4991\u20135000. https:\/\/doi.org\/10.1007\/s00330-020-07561-7 (2021).","journal-title":"Eur. Radiol."},{"key":"26227_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-024-00370-4","author":"Y Brima","year":"2024","unstructured":"Brima, Y. & Atemkeng, M. Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis. BioData Min. https:\/\/doi.org\/10.1186\/s13040-024-00370-4 (2024).","journal-title":"BioData Min."},{"key":"26227_CR21","unstructured":"Rieger, L., Singh, C., Murdoch, W. J. & Yu, B. Interpretations are useful: penalizing explanations to align neural networks with prior knowledge.\u00a0(2019)."},{"key":"26227_CR22","doi-asserted-by":"publisher","first-page":"e200267","DOI":"10.1148\/ryai.2021200267","volume":"3","author":"N Arun","year":"2021","unstructured":"Arun, N. et al. Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radiol. Artif. Intell. 3, e200267. https:\/\/doi.org\/10.1148\/ryai.2021200267 (2021).","journal-title":"Radiol. Artif. Intell."},{"key":"26227_CR23","doi-asserted-by":"publisher","first-page":"119929","DOI":"10.1016\/j.neuroimage.2023.119929","volume":"269","author":"Di Wang","year":"2023","unstructured":"Wang, Di. et al. Deep neural network heatmaps capture Alzheimer\u2019s disease patterns reported in a large meta-analysis of neuroimaging studies. NeuroImage 269, 119929. https:\/\/doi.org\/10.1016\/j.neuroimage.2023.119929 (2023).","journal-title":"NeuroImage"},{"key":"26227_CR24","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1038\/s41746-024-01123-7","volume":"7","author":"EH Leonardsen","year":"2024","unstructured":"Leonardsen, E. H. et al. Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence. NPJ digit. med. 7, 110. https:\/\/doi.org\/10.1038\/s41746-024-01123-7 (2024).","journal-title":"NPJ digit. med."},{"key":"26227_CR25","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3389\/fnins.2019.00509","volume":"13","author":"Y Huang","year":"2019","unstructured":"Huang, Y., Xu, J., Zhou, Y., Tong, T. & Zhuang, X. Diagnosis of alzheimer\u2019s disease via Multi-Modality 3D convolutional neural network. Front. NeuroSci. 13, 509. https:\/\/doi.org\/10.3389\/fnins.2019.00509 (2019).","journal-title":"Front. NeuroSci."},{"key":"26227_CR26","doi-asserted-by":"publisher","first-page":"109039","DOI":"10.1016\/j.compbiomed.2024.109039","volume":"182","author":"M Luo","year":"2024","unstructured":"Luo, M., He, Z., Cui, H., Ward, P. & Chen, Y. P. P. Dual attention based fusion network for MCI conversion prediction. Comput. Biol. Med. 182, 109039. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109039 (2024).","journal-title":"Comput. Biol. Med."},{"key":"26227_CR27","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3389\/fnagi.2018.00111","volume":"10","author":"A Tolonen","year":"2018","unstructured":"Tolonen, A. et al. Data-Driven differential diagnosis of dementia using multiclass disease state index classifier. Front. Aging Neurosci. 10, 111. https:\/\/doi.org\/10.3389\/fnagi.2018.00111 (2018).","journal-title":"Front. Aging Neurosci."},{"key":"26227_CR28","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s13218-020-00636-z","volume":"34","author":"A Holzinger","year":"2020","unstructured":"Holzinger, A., Carrington, A. & M\u00fcller, H. Measuring the quality of explanations: the system causability scale (SCS): comparing human and machine explanations. Kunstliche Intelligenz. 34, 193\u2013198. https:\/\/doi.org\/10.1007\/s13218-020-00636-z (2020).","journal-title":"Kunstliche Intelligenz"},{"key":"26227_CR29","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1017\/S1041610209009405","volume":"21","author":"KA Ellis","year":"2009","unstructured":"Ellis, K. A. et al. The Australian Imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of alzheimer\u2019s disease. Int. Psychogeriatr. 21, 672\u2013687. https:\/\/doi.org\/10.1017\/S1041610209009405 (2009).","journal-title":"Int. Psychogeriatr."},{"key":"26227_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-017-0314-2","author":"F Jessen","year":"2018","unstructured":"Jessen, F. et al. Design and first baseline data of the DZNE multicenter observational study on predementia Alzheimer\u2019s disease (DELCODE). Alzheimers Res. Ther. https:\/\/doi.org\/10.1186\/s13195-017-0314-2 (2018).","journal-title":"Alzheimers Res. Ther."},{"key":"26227_CR31","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.neuroimage.2016.03.067","volume":"144","author":"K Brueggen","year":"2017","unstructured":"Brueggen, K. et al. The European DTI Study on Dementia - A multicenter DTI and MRI study on Alzheimer\u2019s disease and Mild Cognitive Impairment. NeuroImage 144, 305\u2013308. https:\/\/doi.org\/10.1016\/j.neuroimage.2016.03.067 (2017).","journal-title":"NeuroImage"},{"key":"26227_CR32","doi-asserted-by":"publisher","first-page":"4952","DOI":"10.1002\/hbm.24750","volume":"40","author":"F Isensee","year":"2019","unstructured":"Isensee, F. et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum. Brain. Mapp. 40, 4952\u20134964. https:\/\/doi.org\/10.1002\/hbm.24750 (2019).","journal-title":"Hum. Brain. Mapp."},{"key":"26227_CR33","doi-asserted-by":"publisher","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","volume":"219","author":"L Henschel","year":"2020","unstructured":"Henschel, L. et al. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219, 117012. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117012 (2020).","journal-title":"NeuroImage"},{"key":"26227_CR34","doi-asserted-by":"publisher","first-page":"118933","DOI":"10.1016\/j.neuroimage.2022.118933","volume":"251","author":"L Henschel","year":"2022","unstructured":"Henschel, L., K\u00fcgler, D. & Reuter, M. FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI. NeuroImage 251, 118933. https:\/\/doi.org\/10.1016\/j.neuroimage.2022.118933 (2022).","journal-title":"NeuroImage"},{"key":"26227_CR35","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968\u2013980. https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.021 (2006).","journal-title":"NeuroImage"},{"key":"26227_CR36","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3389\/fnins.2012.00171","volume":"6","author":"A Klein","year":"2012","unstructured":"Klein, A. & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Front. NeuroSci. 6, 171. https:\/\/doi.org\/10.3389\/fnins.2012.00171 (2012).","journal-title":"Front. NeuroSci."},{"key":"26227_CR37","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely Connected Convolutional Networks.\u00a0(2016).","DOI":"10.1109\/CVPR.2017.243"},{"key":"26227_CR38","doi-asserted-by":"crossref","unstructured":"Singh, D. et al. Comparison of CNN architectures for detecting Alzheimer\u2019s disease using relevance maps. In:\u00a0T. M. Deserno, (eds).\u00a0Bildverarbeitung f\u00fcr die Medizin 2023.238\u2013243.(Springer Fachmedien Wiesbaden, 2023).","DOI":"10.1007\/978-3-658-41657-7_51"},{"key":"26227_CR39","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S. et al. On Pixel-Wise explanations for Non-Linear classifier decisions by Layer-Wise relevance propagation. PloS One. 10, e0130140. https:\/\/doi.org\/10.1371\/journal.pone.0130140 (2015).","journal-title":"PloS One"},{"key":"26227_CR40","doi-asserted-by":"crossref","unstructured":"Dyrba, M. et al. Comparison of CNN visualization methods to aid model interpretability for detecting alzheimer\u2019s disease. In:\u00a0T. Tolxdorff, (eds). Bildverarbeitung f\u00fcr die Medizin 2020.\u00a0307\u2013312\u00a0(Springer Fachmedien Wiesbaden, 2020).","DOI":"10.1007\/978-3-658-29267-6_68"},{"key":"26227_CR41","doi-asserted-by":"crossref","unstructured":"Montavon, G., Binder, A., Lapuschkin, S., Samek, W. & M\u00fcller, K. R. Layer-wise relevance propagation: An overview. In: W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen & K.-R. M\u00fcller (Eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.\u00a011700, 193\u2013209. (Springer International Publishing, 2019).","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"26227_CR42","doi-asserted-by":"crossref","unstructured":"Kohlbrenner, M. et al. Towards Best Practice in Explaining Neural Network Decisions with LRP.\u00a0(2019).","DOI":"10.1109\/IJCNN48605.2020.9206975"},{"key":"26227_CR43","doi-asserted-by":"crossref","unstructured":"Hiller, B. C. et al. Evaluating the fidelity of explanations for convolutional neural networks in alzheimer\u2019s disease detection. In: C. Palm, et al. (eds).\u00a0Bildverarbeitung f\u00fcr die Medizin 2025.\u00a076\u201381 (Springer Fachmedien Wiesbaden, 2025).","DOI":"10.1007\/978-3-658-47422-5_18"},{"key":"26227_CR44","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via Gradient-based localization.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1610.02391 (2016).","DOI":"10.1109\/ICCV.2017.74"},{"key":"26227_CR45","doi-asserted-by":"crossref","unstructured":"Zeiler, M. D. & Fergus, R. Visualizing and Understanding Convolutional Networks.\u00a0(2013).","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"26227_CR46","unstructured":"Sundararajan, M., Taly, A. & Yan, Q. Axiomatic Attribution for Deep Networks. (2017)."},{"key":"26227_CR47","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10278-022-00719-3","volume":"36","author":"LA de Santi","year":"2023","unstructured":"de Santi, L. A., Pasini, E., Santarelli, M. F., Genovesi, D. & Positano, V. An explainable convolutional neural network for the early diagnosis of alzheimer\u2019s disease from 18F-FDG PET. J. Digit. Imaging. 36, 189\u2013203. https:\/\/doi.org\/10.1007\/s10278-022-00719-3 (2023).","journal-title":"J. Digit. Imaging"},{"key":"26227_CR48","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1002\/ima.22657","volume":"32","author":"T Pohl","year":"2022","unstructured":"Pohl, T., Jakab, M. & Benesova, W. Interpretability of deep neural networks used for the diagnosis of alzheimer\u2019s disease. Int. J. Imaging Syst. Tech. 32, 673\u2013686. https:\/\/doi.org\/10.1002\/ima.22657 (2022).","journal-title":"Int. J. Imaging Syst. Tech."},{"key":"26227_CR49","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-021-00924-2","author":"M Dyrba","year":"2021","unstructured":"Dyrba, M. et al. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer\u2019s disease. Alzheimers Res. Ther. https:\/\/doi.org\/10.1186\/s13195-021-00924-2 (2021).","journal-title":"Alzheimers Res. Ther."},{"key":"26227_CR50","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3389\/fnagi.2019.00194","volume":"11","author":"M B\u00f6hle","year":"2019","unstructured":"B\u00f6hle, M., Eitel, F., Weygandt, M. & Ritter, K. Layer-Wise relevance propagation for explaining deep neural network decisions in MRI-Based alzheimer\u2019s disease classification. Front. Aging Neurosci. 11, 194. https:\/\/doi.org\/10.3389\/fnagi.2019.00194 (2019).","journal-title":"Front. Aging Neurosci."},{"key":"26227_CR51","doi-asserted-by":"crossref","unstructured":"Eitel, F. et al. Testing the robustness of attribution methods for convolutional neural networks in MRI-based alzheimer\u2019s disease classification. In: K. Suzuki,\u00a0et al. (eds).\u00a0Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support.\u00a011797 3\u201311 (Springer International Publishing, 2019).","DOI":"10.1007\/978-3-030-33850-3_1"},{"key":"26227_CR52","doi-asserted-by":"publisher","DOI":"10.1177\/13872877251331222","author":"D Singh","year":"2025","unstructured":"Singh, D. et al. A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans. J. Alzheimer\u2019s disease: JAD https:\/\/doi.org\/10.1177\/13872877251331222 (2025).","journal-title":"J. Alzheimer\u2019s disease: JAD"},{"key":"26227_CR53","doi-asserted-by":"publisher","first-page":"731","DOI":"10.3233\/JAD-161197","volume":"65","author":"M Dyrba","year":"2018","unstructured":"Dyrba, M. et al. Comparison of different hypotheses regarding the spread of alzheimer\u2019s disease using Markov random fields and multimodal imaging. J. Alzheimer\u2019s Disease: JAD. 65, 731\u2013746. https:\/\/doi.org\/10.3233\/JAD-161197 (2018).","journal-title":"J. Alzheimer\u2019s Disease: JAD"},{"key":"26227_CR54","doi-asserted-by":"crossref","unstructured":"Boccardi, M. et al. The MRI pattern of frontal and Temporal brain atrophy in fronto-temporal dementia. Neurobiol. Aging 24, 95\u2013103. https:\/\/doi.org\/10.1016\/S0197-4580(02)00045\u20133 (2003).","DOI":"10.1016\/S0197-4580(02)00045-3"},{"key":"26227_CR55","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1212\/wnl.49.3.786","volume":"49","author":"CR Jack","year":"1997","unstructured":"Jack, C. R. et al. Medial Temporal atrophy on MRI in normal aging and very mild alzheimer\u2019s disease. Neurology 49, 786\u2013794. https:\/\/doi.org\/10.1212\/wnl.49.3.786 (1997).","journal-title":"Neurology"},{"key":"26227_CR56","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.eswa.2018.04.037","volume":"108","author":"N Bi","year":"2018","unstructured":"Bi, N., Tan, J., Lai, J. H. & Suen, C. Y. High-dimensional supervised feature selection via optimized kernel mutual information. Expert Syst. Appl. 108, 81\u201395. https:\/\/doi.org\/10.1016\/j.eswa.2018.04.037 (2018).","journal-title":"Expert Syst. Appl."},{"key":"26227_CR57","unstructured":"Rosenberg, A. & Hirschberg, J., V-Measure:\u00a0Conditional Entropy-Based External Cluster Evaluation Measure.\u00a0410\u2013420 (2007)."},{"key":"26227_CR58","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.5555\/1756006.1953024","volume":"11","author":"X Nguyen","year":"2010","unstructured":"Nguyen, X., Epps, J. & Bailey, J. Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance. J. Mach. Learn. Res. 11, 2837\u20132854. https:\/\/doi.org\/10.5555\/1756006.1953024 (2010).","journal-title":"J. Mach. Learn. Res."},{"key":"26227_CR59","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193\u2013218. https:\/\/doi.org\/10.1007\/BF01908075 (1985).","journal-title":"J. Classif."},{"key":"26227_CR60","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11634-022-00491-w","volume":"17","author":"JE Chac\u00f3n","year":"2023","unstructured":"Chac\u00f3n, J. E. & Rastrojo, A. I. Minimum adjusted Rand index for two clusterings of a given size. Adv. Data Anal. Classif. 17, 125\u2013133. https:\/\/doi.org\/10.1007\/s11634-022-00491-w (2023).","journal-title":"Adv. Data Anal. Classif."},{"key":"26227_CR61","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1080\/01621459.1983.10478008","volume":"78","author":"EB Fowlkes","year":"1983","unstructured":"Fowlkes, E. B. & Mallows, C. L. A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78, 553\u2013569. https:\/\/doi.org\/10.1080\/01621459.1983.10478008 (1983).","journal-title":"J. Am. Stat. Assoc."},{"key":"26227_CR62","doi-asserted-by":"crossref","unstructured":"Rousseeuw, P. J. & Silhouettes A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365. https:\/\/doi.org\/10.1016\/0377\u20130427(87)90125-7 (1987).","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"26227_CR63","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"PAMI-1","author":"DL Davies","year":"1979","unstructured":"Davies, D. L. & Bouldin, D. W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1, 224\u2013227. https:\/\/doi.org\/10.1109\/TPAMI.1979.4766909 (1979).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26227_CR64","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward, J. H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236\u2013244. https:\/\/doi.org\/10.1080\/01621459.1963.10500845 (1963).","journal-title":"J. Am. Stat. Assoc."},{"key":"26227_CR65","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s13195-021-00785-9","volume":"13","author":"F Levin","year":"2021","unstructured":"Levin, F. et al. Data-driven FDG-PET subtypes of Alzheimer\u2019s disease-related neurodegeneration. Alzheimers Res. Ther. 13, 49. https:\/\/doi.org\/10.1186\/s13195-021-00785-9 (2021).","journal-title":"Alzheimers Res. Ther."},{"key":"26227_CR66","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1093\/brain\/aww142","volume":"139","author":"AM Racine","year":"2016","unstructured":"Racine, A. M. et al. Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife. Brain: J. Neurol. 139, 2261\u20132274. https:\/\/doi.org\/10.1093\/brain\/aww142 (2016).","journal-title":"Brain: J. Neurol."},{"key":"26227_CR67","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar, R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6, 21\u201345. https:\/\/doi.org\/10.1109\/MCAS.2006.1688199 (2006).","journal-title":"IEEE Circuits Syst. Mag"},{"key":"26227_CR68","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1093\/jamia\/ocz169","volume":"27","author":"PI Dissanayake","year":"2020","unstructured":"Dissanayake, P. I., Colicchio, T. K. & Cimino, J. J. Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis. J. Am. Med. Inf. Association: JAMIA. 27, 159\u2013174. https:\/\/doi.org\/10.1093\/jamia\/ocz169 (2020).","journal-title":"J. Am. Med. Inf. Association: JAMIA"},{"key":"26227_CR69","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.jalz.2013.02.009","volume":"10","author":"A Malhotra","year":"2014","unstructured":"Malhotra, A. et al. ADO: a disease ontology representing the domain knowledge specific to alzheimer\u2019s disease. Alzheimer\u2019s Dement. J. Alzheimer\u2019s Assoc. 10, 238\u2013246. https:\/\/doi.org\/10.1016\/j.jalz.2013.02.009 (2014).","journal-title":"Alzheimer\u2019s Dement. J. Alzheimer\u2019s Assoc."},{"key":"26227_CR70","doi-asserted-by":"crossref","unstructured":"Singh, D. & Dyrba, M. Computational ontology and visualization framework for the visual comparison of brain atrophy profiles. In: A. Maier, et al. (eds).\u00a0Bildverarbeitung f\u00fcr die Medizin 2024.\u00a0 149\u2013154\u00a0(Springer Fachmedien Wiesbaden, 2024).","DOI":"10.1007\/978-3-658-44037-4_43"},{"key":"26227_CR71","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta, A. et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fusion. 58, 82\u2013115. https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012 (2020).","journal-title":"Inform. Fusion"},{"key":"26227_CR72","unstructured":"Krug, V. Neuroscience-inspired Analysis and Visualization of Deep Neural Networks (Universit\u00e4ts- und Landesbibliothek Sachsen-Anhalt, 2024)."},{"key":"26227_CR73","doi-asserted-by":"crossref","unstructured":"Krug, V., Ratul, R. K., Olson, C. & Stober, S. Visualizing deep neural networks with topographic activation maps. In: P. Lukowicz, S. Mayer, J. Koch, J. Shawe-Taylor & I. Tiddi (eds).\u00a0HHAI 2023: Augmenting Human Intellect.\u00a0(IOS Press, 2023).","DOI":"10.3233\/FAIA230080"},{"key":"26227_CR74","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.3233\/JAD-210064","volume":"84","author":"M Shojaie","year":"2021","unstructured":"Shojaie, M. et al. PET imaging of Tau pathology and Amyloid-\u03b2, and MRI for alzheimer\u2019s disease feature fusion and multimodal classification. J. Alzheimer\u2019s Disease: JAD. 84, 1497\u20131514. https:\/\/doi.org\/10.3233\/JAD-210064 (2021).","journal-title":"J. Alzheimer\u2019s Disease: JAD"},{"key":"26227_CR75","doi-asserted-by":"publisher","first-page":"637386","DOI":"10.3389\/fdgth.2021.637386","volume":"3","author":"J Song","year":"2021","unstructured":"Song, J. et al. An effective multimodal image fusion method using MRI and PET for alzheimer\u2019s disease diagnosis. Front. Digit. Health. 3, 637386. https:\/\/doi.org\/10.3389\/fdgth.2021.637386 (2021).","journal-title":"Front. Digit. Health"},{"key":"26227_CR76","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11040369","author":"C Metta","year":"2024","unstructured":"Metta, C., Beretta, A., Pellungrini, R., Rinzivillo, S. & Giannotti, F. Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence. Bioengineering (Basel, Switzerland) https:\/\/doi.org\/10.3390\/bioengineering11040369 (2024).","journal-title":"Bioengineering (Basel, Switzerland)"},{"key":"26227_CR77","doi-asserted-by":"crossref","unstructured":"Duell, J., Fan, X. & Seisenberger, M. Towards polynomial adaptive local explanations for healthcare classifiers. In: M. Ceci, S. Flesca, E. Masciari, G. Manco & Z. W. Ra\u015b (eds). Foundations of Intelligent Systems.\u00a0411\u2013420 (Springer International Publishing, 2022).","DOI":"10.1007\/978-3-031-16564-1_39"},{"key":"26227_CR78","doi-asserted-by":"publisher","DOI":"10.1056\/AIcs2300235","author":"D Ferber","year":"2024","unstructured":"Ferber, D. et al. GPT-4 for information retrieval and comparison of medical oncology guidelines. NEJM AI https:\/\/doi.org\/10.1056\/AIcs2300235 (2024).","journal-title":"NEJM AI"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26227-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26227-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26227-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T22:02:31Z","timestamp":1763330551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26227-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,12]]},"references-count":78,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["26227"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-26227-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,12]]},"assertion":[{"value":"9 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"S.Teipel: was serving on advisory boards of Eisai, Lilly, and GE Healthcare. He is member of the independent data safety and monitoring board of the study ENVISION (Biogen). A.Hermann: received honoraria for presentations and participation in advisory boards from Amylyx and IFT Pharma. He has received royalties from Elsevier Press and Kohlhammer. E.Duezel: Paid consultancy work and talks for Roche, Lilly, Eisai, Biogen, neotiv, and UCLC; Holds shares of neotiv. O.Peters: Paid consultancy work and talks for Biogen, Eisai, Grifols, Lilly, Noselab, Prinnovation, Schwabe, and Roche. J.Wiltfang: Paid consultancy and talks for Abbott, Actelion, Amgen, Beijing Yibai Science and Technology Ltd., Biogen, Boehringer Ingelheim, Gloryren, Immungenetics, Janssen Cilag, Lilly, Med Update GmbH, MSD Sharp & Dohme, Noselab, Pfizer, Roche, and Roboscreen; holds patents PCT\/EP2011 001724 and PCT\/EP 2015 052945 and also supported by an Ilidio Pinho professorship, iBiMED (UIDB\/04501\/2020) at the University of Aveiro, Portugal. J.Priller: serves on the TSC of the Sinapps2 study and holds patents on EPO variants. F.Jessen: received fees for consultation from Eli Lilly, Novartis, Roche, BioGene, MSD, Piramal, Janssen, and Lundbeck. M.Synofzik: received consultancy honoraria from Ionis, UCB, Prevail, Orphazyme, Servier, Reata, GenOrph, AviadoBio, Biohaven, Zevra, and Lilly, all unrelated to the present manuscript. J.Levin: speaker fees from Bayer Vital, Biogen, EISAI, TEVA, and Roche, consulting fees from Axon Neuroscience and Biogen, author fees from Thieme medical publishers. These financial interests caused no effects on the study design, data collection and analysis, decision to publish, or preparation of the manuscript. All other authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39554"}}