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000281857 0247_ $$2doi$$a10.1177/13872877251331222
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000281857 0247_ $$2ISSN$$a1387-2877
000281857 0247_ $$2ISSN$$a1875-8908
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000281857 1001_ $$0P:(DE-2719)9002353$$aSingh, Devesh$$b0$$eFirst author
000281857 245__ $$aA computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.
000281857 260__ $$aAmsterdam$$bIOS Press$$c2025
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000281857 520__ $$aBackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.
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000281857 650_7 $$2Other$$aAlzheimer's disease
000281857 650_7 $$2Other$$abrain volumetry
000281857 650_7 $$2Other$$acomputer graphics
000281857 650_7 $$2Other$$afrontotemporal dementia
000281857 650_7 $$2Other$$amagnetic resonance imaging
000281857 650_7 $$2Other$$aneuroanatomy
000281857 650_7 $$2Other$$aontology
000281857 650_2 $$2MeSH$$aHumans
000281857 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000281857 650_2 $$2MeSH$$aAlzheimer Disease: pathology
000281857 650_2 $$2MeSH$$aAlzheimer Disease: diagnostic imaging
000281857 650_2 $$2MeSH$$aBrain: pathology
000281857 650_2 $$2MeSH$$aBrain: diagnostic imaging
000281857 650_2 $$2MeSH$$aMale
000281857 650_2 $$2MeSH$$aFemale
000281857 650_2 $$2MeSH$$aAged
000281857 650_2 $$2MeSH$$aFrontotemporal Dementia: pathology
000281857 650_2 $$2MeSH$$aFrontotemporal Dementia: diagnostic imaging
000281857 650_2 $$2MeSH$$aAtrophy: pathology
000281857 650_2 $$2MeSH$$aNeural Networks, Computer
000281857 650_2 $$2MeSH$$aMiddle Aged
000281857 7001_ $$0P:(DE-2719)9001307$$aGrazia, Alice$$b1
000281857 7001_ $$00000-0003-1446-9670$$aReiz, Achim$$b2
000281857 7001_ $$0P:(DE-2719)2811732$$aHermann, Andreas$$b3
000281857 7001_ $$0P:(DE-2719)2811720$$aAltenstein, Slawek$$b4
000281857 7001_ $$00009-0000-9259-9230$$aBeichert, Lukas$$b5
000281857 7001_ $$0P:(DE-2719)9002620$$aBernhardt, Alexander$$b6
000281857 7001_ $$0P:(DE-2719)2811351$$aBuerger, Katharina$$b7$$udzne
000281857 7001_ $$0P:(DE-2719)9001011$$aButryn, Michaela$$b8$$udzne
000281857 7001_ $$00009-0006-4005-3352$$aDechent, Peter$$b9
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000281857 7001_ $$0P:(DE-2719)9000543$$aEwers, Michael$$b11
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000281857 7001_ $$0P:(DE-2719)2812392$$aFreiesleben, Silka D$$b13
000281857 7001_ $$0P:(DE-2719)2811614$$aGlanz, Wenzel$$b14
000281857 7001_ $$00000-0002-1773-1518$$aHetzer, Stefan$$b15
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000281857 7001_ $$0P:(DE-2719)2810394$$aKilimann, Ingo$$b17
000281857 7001_ $$0P:(DE-2719)9002382$$aKimmich, Okka$$b18$$udzne
000281857 7001_ $$0P:(DE-2719)2000055$$aLaske, Christoph$$b19$$udzne
000281857 7001_ $$0P:(DE-2719)2811659$$aLevin, Johannes$$b20
000281857 7001_ $$0P:(DE-2719)2811189$$aLohse, Andrea$$b21
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000281857 7001_ $$0P:(DE-2719)9001516$$aMunk, Matthias$$b23
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000281857 7001_ $$0P:(DE-2719)2811122$$aPriller, Josef$$b27
000281857 7001_ $$0P:(DE-2719)2380559$$aPrudlo, Johannes$$b28$$udzne
000281857 7001_ $$0P:(DE-2719)9001808$$aRauchmann, Boris Stephan$$b29$$udzne
000281857 7001_ $$00000-0001-5189-134X$$aRostamzadeh, Ayda$$b30
000281857 7001_ $$0P:(DE-2719)2812341$$aRoy-Kluth, Nina$$b31$$udzne
000281857 7001_ $$00000-0001-6316-8773$$aScheffler, Klaus$$b32
000281857 7001_ $$0P:(DE-2719)2812035$$aSchneider, Anja$$b33$$udzne
000281857 7001_ $$0P:(DE-2719)9002878$$aSchneider, Luisa-Sophie$$b34$$udzne
000281857 7001_ $$0P:(DE-2719)2814326$$aSchott, Björn H$$b35
000281857 7001_ $$0P:(DE-2719)2811324$$aSpottke, Annika$$b36$$udzne
000281857 7001_ $$0P:(DE-2719)2812446$$aSpruth, Eike Jakob$$b37$$udzne
000281857 7001_ $$0P:(DE-2719)2811275$$aSynofzik, Matthis$$b38
000281857 7001_ $$0P:(DE-2719)2811317$$aWiltfang, Jens$$b39
000281857 7001_ $$0P:(DE-2719)2000032$$aJessen, Frank$$b40$$udzne
000281857 7001_ $$0P:(DE-2719)2000026$$aTeipel, Stefan J$$b41
000281857 7001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b42$$eLast author
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000281857 7001_ $$agroups, study$$b46$$eCollaboration Author
000281857 773__ $$0PERI:(DE-600)2070772-1$$a10.1177/13872877251331222$$gVol. 108, no. 1_suppl$$n1_suppl$$pS258 - S273$$tJournal of Alzheimer's disease$$v108$$x1387-2877$$y2025
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