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Cited by 69Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareFunctional Brain Connectivity Studies

Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study

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Klöppel, Stefan, Peter, Jessica, Ludl, Anna, Pilatus, Anne, Maier, Sabrina, Mader, Irina, Heimbach, Bernhard, Frings, Lars +11 more·2015·Journal of Alzheimer’s Disease
DOI 10.3233/jad-150334PMC PMC4923764
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Abstract

Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4 ). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC > 0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies

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  • Koppel et al. were able to separate healthy elderly from patients with dementia with an AUC of 0.97 in 134 cases with AD, FTD, LBD and MCI.

    “Koppel et al. compared 134 cases with AD, FTD, LBD and MCI and were able to separate healthy elderly from patients with dementia with an AUC of 0.97 [16].”

    From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [16]

    The Results (Differential diagnostics of dementia; page unavailable) report an AUC of 0.97 for separating healthy controls from the other diagnostic groups. However, the Abstract distinguishes the 134 local-clinic cases used for differential diagnosis from the separate MCI progression analysis, so describing all 134 as cases with AD, FTD, LBD and MCI is not supported. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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References 73 · Cited by 69

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Authors
Klöppel, S., Peter, J., Ludl, A., Pilatus, A., Maier, S., Mader, I., Heimbach, B., Frings, L., Egger, K., Dukart, J., Schroeter, M. L., Perneczky, R., Häussermann, P., Vach, W., Urbach, H., Teipel, S., Hüll, M., Abdulkadir, A., & 1, A. F. T. A. D. N. I.
Affiliations
  • University Medical Center Freiburg (DE)
  • F. Hoffmann-La Roche AG (CH)
  • Max Planck Institute for Human Cognitive and Brain Sciences (DE)
  • West London Mental Health NHS Trust (GB)
  • LVR-Klinik Köln (DE)
  • University of Freiburg (DE)
  • German Center for Neurodegenerative Diseases (DE)
Corresponding author
Klöppel, Stefan
ORCID
Venue
Journal of Alzheimer’s Disease
Publisher
SAGE Publications
Published
2015-08-11
Volume
47
Issue
4
Pages
939-954
ISSN
  • 1387-2877 (ISSN-L)
  • 1875-8908
DOI
10.3233/jad-150334
PMC
PMC4923764
OpenAlex
W2263292588
Cited by
69
References
73
Citations by year
2026: 4 · 2025: 2 · 2024: 4 · 2023: 9 · 2022: 4 · 2021: 9 · 2020: 11 · 2019: 10 · 2018: 6 · 2017: 4 · 2016: 5 · 2015: 1
FWCI
4.33
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Journal of Alzheimer s Disease (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
  • eScholarship (California Digital Library) (submittedVersion)
  • Max Planck Digital Library (submittedVersion, other-oa)
  • FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
  • DZNE Pub (submittedVersion, cc-by-nc)
Funders
  • U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
  • Foundation for the National Institutes of Health (W81XWH-12-2-0012, U01AG024904, Grant U01 AG024904, AG024904)
  • Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
  • Parkinson's Disease Foundation (PDF‐IRG‐1307)
  • Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • University of Southern California (W81XWH-12-2-0012, U01 AG024904)
  • F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • Medpace (U01 AG024904)
  • Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Synarc (U01 AG024904)
  • Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-, Department of Defense award number W81XWH-12-2-0012)
  • Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
  • Deutsche Forschungsgemeinschaft
  • Bundesministerium für Bildung und Forschung
  • Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Bundesministerium für Wirtschaft und Energie
  • Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, Grant U01 AG024904, AG024904)
  • Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • University of California, San Diego
  • Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
  • National Institute on Aging (U01-AG-024904, W81XWH-12-2–0012, AG 024904, P30AG013846, P50AG005134, P30AG062421)
  • National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
Alzheimer's disease
MeSH terms
Aged, Aging, Alzheimer Disease, Brain, Diagnosis, Differential, Female, Follow-Up Studies, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Prospective Studies, Linear Models, Disease Progression, Lewy Body Disease, Imaging, Three-Dimensional, Frontotemporal Dementia, Support Vector Machine, White Matter

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