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000282290 1001_ $$0P:(DE-2719)9002353$$aSingh, Devesh$$b0$$eFirst author$$udzne
000282290 245__ $$aAn unsupervised XAI framework for dementia detection with context enrichment.
000282290 260__ $$a[London]$$bSpringer Nature$$c2025
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000282290 520__ $$aExplainable 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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000282290 650_7 $$2Other$$aAlzheimer’s disease
000282290 650_7 $$2Other$$aBrain volumetry
000282290 650_7 $$2Other$$aExplainable artificial intelligence (XAI)
000282290 650_7 $$2Other$$aFrontotemporal dementia
000282290 650_7 $$2Other$$aMagnetic resonance imaging
000282290 650_7 $$2Other$$aNeurodegenerative diseases
000282290 650_7 $$2Other$$aQualitative evaluation
000282290 650_2 $$2MeSH$$aHumans
000282290 650_2 $$2MeSH$$aDementia: diagnosis
000282290 650_2 $$2MeSH$$aDementia: diagnostic imaging
000282290 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000282290 650_2 $$2MeSH$$aMale
000282290 650_2 $$2MeSH$$aFemale
000282290 650_2 $$2MeSH$$aAged
000282290 650_2 $$2MeSH$$aCognitive Dysfunction: diagnostic imaging
000282290 650_2 $$2MeSH$$aCognitive Dysfunction: diagnosis
000282290 650_2 $$2MeSH$$aAlzheimer Disease: diagnostic imaging
000282290 650_2 $$2MeSH$$aAlzheimer Disease: diagnosis
000282290 650_2 $$2MeSH$$aNeural Networks, Computer
000282290 650_2 $$2MeSH$$aArtificial Intelligence
000282290 650_2 $$2MeSH$$aBrain: diagnostic imaging
000282290 650_2 $$2MeSH$$aBrain: pathology
000282290 650_2 $$2MeSH$$aAged, 80 and over
000282290 7001_ $$0P:(DE-2719)9003603$$aBrima, Yusuf$$b1$$udzne
000282290 7001_ $$0P:(DE-2719)2814217$$aLevin, Fedor$$b2$$udzne
000282290 7001_ $$aBecker, Martin$$b3
000282290 7001_ $$aHiller, Bjarne$$b4
000282290 7001_ $$0P:(DE-2719)2811732$$aHermann, Andreas$$b5$$udzne
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000282290 7001_ $$0P:(DE-2719)9002620$$aBernhardt, Alexander$$b8$$udzne
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000282290 7001_ $$0P:(DE-2719)9000543$$aEwers, Michael$$b13
000282290 7001_ $$0P:(DE-2719)2811326$$aFliessbach, Klaus$$b14$$udzne
000282290 7001_ $$0P:(DE-2719)2812392$$aFreiesleben, Silka D$$b15$$udzne
000282290 7001_ $$0P:(DE-2719)2811614$$aGlanz, Wenzel$$b16$$udzne
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000282290 7001_ $$0P:(DE-2719)9002382$$aKimmich, Okka$$b21$$udzne
000282290 7001_ $$0P:(DE-2719)2000055$$aLaske, Christoph$$b22$$udzne
000282290 7001_ $$0P:(DE-2719)2811659$$aLevin, Johannes$$b23$$udzne
000282290 7001_ $$0P:(DE-2719)2811189$$aLohse, Andrea$$b24
000282290 7001_ $$0P:(DE-2719)9002520$$aLuesebrink, Falk$$b25$$udzne
000282290 7001_ $$0P:(DE-2719)9001516$$aMunk, Matthias$$b26$$udzne
000282290 7001_ $$0P:(DE-2719)2812234$$aPerneczky, Robert$$b27$$udzne
000282290 7001_ $$0P:(DE-2719)2811024$$aPeters, Oliver$$b28$$udzne
000282290 7001_ $$0P:(DE-2719)9000703$$aPreis, Lukas$$b29$$udzne
000282290 7001_ $$0P:(DE-2719)2811122$$aPriller, Josef$$b30$$udzne
000282290 7001_ $$0P:(DE-2719)2380559$$aPrudlo, Johannes$$b31$$udzne
000282290 7001_ $$0P:(DE-HGF)0$$aPrychynenko, Diana$$b32
000282290 7001_ $$0P:(DE-2719)9001808$$aRauchmann, Boris Stephan$$b33$$udzne
000282290 7001_ $$0P:(DE-HGF)0$$aRostamzadeh, Ayda$$b34
000282290 7001_ $$0P:(DE-2719)2812341$$aRoy-Kluth, Nina$$b35$$udzne
000282290 7001_ $$0P:(DE-HGF)0$$aScheffler, Klaus$$b36
000282290 7001_ $$0P:(DE-2719)2812035$$aSchneider, Anja$$b37$$udzne
000282290 7001_ $$0P:(DE-HGF)0$$aDroste Zu Senden, Louise$$b38
000282290 7001_ $$0P:(DE-2719)2814326$$aSchott, Björn H$$b39$$udzne
000282290 7001_ $$0P:(DE-2719)2811324$$aSpottke, Annika$$b40$$udzne
000282290 7001_ $$0P:(DE-2719)2811275$$aSynofzik, Matthis$$b41$$udzne
000282290 7001_ $$0P:(DE-2719)2811317$$aWiltfang, Jens$$b42$$udzne
000282290 7001_ $$0P:(DE-2719)2000032$$aJessen, Frank$$b43$$udzne
000282290 7001_ $$aWeber, Marc-André$$b44
000282290 7001_ $$0P:(DE-2719)2000026$$aTeipel, Stefan J$$b45$$udzne
000282290 7001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b46$$eLast author$$udzne
000282290 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-025-26227-2$$gVol. 15, no. 1, p. 39554$$n1$$p39554$$tScientific reports$$v15$$x2045-2322$$y2025
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