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001     281857
005     20251118102958.0
024 7 _ |a 10.1177/13872877251331222
|2 doi
024 7 _ |a pmid:40255031
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024 7 _ |a pmc:PMC12583655
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024 7 _ |a 1387-2877
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024 7 _ |a 1875-8908
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037 _ _ |a DZNE-2025-01230
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Singh, Devesh
|0 P:(DE-2719)9002353
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245 _ _ |a A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.
260 _ _ |a Amsterdam
|c 2025
|b IOS Press
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 BackgroundConvolutional 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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650 _ 7 |a Alzheimer's disease
|2 Other
650 _ 7 |a brain volumetry
|2 Other
650 _ 7 |a computer graphics
|2 Other
650 _ 7 |a frontotemporal dementia
|2 Other
650 _ 7 |a magnetic resonance imaging
|2 Other
650 _ 7 |a neuroanatomy
|2 Other
650 _ 7 |a ontology
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Magnetic Resonance Imaging: methods
|2 MeSH
650 _ 2 |a Alzheimer Disease: pathology
|2 MeSH
650 _ 2 |a Alzheimer Disease: diagnostic imaging
|2 MeSH
650 _ 2 |a Brain: pathology
|2 MeSH
650 _ 2 |a Brain: diagnostic imaging
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Frontotemporal Dementia: pathology
|2 MeSH
650 _ 2 |a Frontotemporal Dementia: diagnostic imaging
|2 MeSH
650 _ 2 |a Atrophy: pathology
|2 MeSH
650 _ 2 |a Neural Networks, Computer
|2 MeSH
650 _ 2 |a Middle Aged
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700 1 _ |a Grazia, Alice
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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 10.1177/13872877251331222
|g Vol. 108, no. 1_suppl
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|t Journal of Alzheimer's disease
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|y 2025
|x 1387-2877
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