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001     282290
005     20251114125718.0
024 7 _ |a 10.1038/s41598-025-26227-2
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024 7 _ |a pmid:41224940
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024 7 _ |a pmc:PMC12612133
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037 _ _ |a DZNE-2025-01261
041 _ _ |a English
082 _ _ |a 600
100 1 _ |a Singh, Devesh
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245 _ _ |a An unsupervised XAI framework for dementia detection with context enrichment.
260 _ _ |a [London]
|c 2025
|b Springer Nature
336 7 _ |a article
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336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a Explainable Artificial Intelligence (XAI) methods enhance the diagnostic efficiency of clinical decision support systems by making the predictions of a convolutional neural network's (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 = 3253), including participants that were cognitively normal, with amnestic mild cognitive impairment, dementia due to Alzheimer's 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 = 6) 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' 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.
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650 _ 7 |a Alzheimer’s disease
|2 Other
650 _ 7 |a Brain volumetry
|2 Other
650 _ 7 |a Explainable artificial intelligence (XAI)
|2 Other
650 _ 7 |a Frontotemporal dementia
|2 Other
650 _ 7 |a Magnetic resonance imaging
|2 Other
650 _ 7 |a Neurodegenerative diseases
|2 Other
650 _ 7 |a Qualitative evaluation
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Dementia: diagnosis
|2 MeSH
650 _ 2 |a Dementia: diagnostic imaging
|2 MeSH
650 _ 2 |a Magnetic Resonance Imaging: methods
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Cognitive Dysfunction: diagnostic imaging
|2 MeSH
650 _ 2 |a Cognitive Dysfunction: diagnosis
|2 MeSH
650 _ 2 |a Alzheimer Disease: diagnostic imaging
|2 MeSH
650 _ 2 |a Alzheimer Disease: diagnosis
|2 MeSH
650 _ 2 |a Neural Networks, Computer
|2 MeSH
650 _ 2 |a Artificial Intelligence
|2 MeSH
650 _ 2 |a Brain: diagnostic imaging
|2 MeSH
650 _ 2 |a Brain: pathology
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650 _ 2 |a Aged, 80 and over
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700 1 _ |a Priller, Josef
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700 1 _ |a Prudlo, Johannes
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700 1 _ |a Prychynenko, Diana
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700 1 _ |a Rauchmann, Boris Stephan
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700 1 _ |a Rostamzadeh, Ayda
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700 1 _ |a Roy-Kluth, Nina
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700 1 _ |a Scheffler, Klaus
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700 1 _ |a Droste Zu Senden, Louise
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700 1 _ |a Schott, Björn H
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700 1 _ |a Spottke, Annika
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700 1 _ |a Synofzik, Matthis
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700 1 _ |a Jessen, Frank
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700 1 _ |a Weber, Marc-André
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700 1 _ |a Teipel, Stefan J
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700 1 _ |a Dyrba, Martin
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773 _ _ |a 10.1038/s41598-025-26227-2
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