Leonie Lampe, Sebastian Niehaus, Hans-Jürgen Huppertz, Alberto Merola, Janis Dominik Reinelt, Karsten Mueller, Sarah Straub, Klaus Fassbender, Klaus Fliessbach, Holger Jahn, Johannes Kornhuber, Martin Konrad Lauer, Johannes Prudlo, Anja Schneider, Matthis Synofzik, Adrian Danek, Janine Diehl-Schmid, Markus Otto, Arno Villringer, Karl Egger, Elke Hattingen, Rüdiger Hilker-Roggendorf, Alfons Schnitzler, Martin Südmeyer, Wolfgang H. Oertel, Jan Rainer Kassubek, Günter Höglinger, Matthias Schroeter
- Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
MetadatenAuthor: | Leonie LampeGND, Sebastian Niehaus, Hans-Jürgen HuppertzORCiDGND, Alberto Merola, Janis Dominik ReineltGND, Karsten MuellerGND, Sarah StraubGND, Klaus FassbenderGND, Klaus FliessbachGND, Holger JahnORCiDGND, Johannes KornhuberORCiDGND, Martin Konrad LauerGND, Johannes PrudloGND, Anja Schneider, Matthis SynofzikORCiDGND, Adrian DanekGND, Janine Diehl-SchmidORCiDGND, Markus OttoORCiDGND, Arno VillringerORCiDGND, Karl EggerORCiDGND, Elke HattingenORCiDGND, Rüdiger Hilker-RoggendorfGND, Alfons SchnitzlerORCiDGND, Martin SüdmeyerGND, Wolfgang H. OertelORCiDGND, Jan Rainer KassubekORCiDGND, Günter HöglingerORCiDGND, Matthias SchroeterORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-836347 |
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DOI: | https://doi.org/10.1186/s13195-022-00983-z |
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ISSN: | 1758-9193 |
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Parent Title (English): | Alzheimer's research & therapy |
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Publisher: | BioMed Central |
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Place of publication: | London |
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Document Type: | Article |
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Language: | English |
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Date of Publication (online): | 2022/05/03 |
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Date of first Publication: | 2022/05/03 |
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Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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Contributing Corporation: | FTLD-Consortium Germany; German Atypical Parkinson Consortium Study Group |
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Release Date: | 2024/04/23 |
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Tag: | Comparative analysis; Deep neural network; Gradient boosting; Multi-syndrome classification; Neurodegenerative syndromes; Random forest; Support vector machine |
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Volume: | 14.2022 |
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Issue: | art. 62 |
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Article Number: | 62 |
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Page Number: | 13 |
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First Page: | 1 |
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Last Page: | 13 |
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Note: | This work was supported by the German Federal Ministry of Education and Research (BMBF) by a grant given to the German FTLD Consortium (FKZ O1GI1007A), by the German Research Foundation DFG (SCHR 774/5-1), by the Parkinson’s Disease Foundation (PDF-IRG-1307), and by the Michael J. Fox Foundation (MJFF-11362). |
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Note: | Project repository: https://github.com/Leoniela/Comparison-ML-Algorithms-Neurodegen |
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HeBIS-PPN: | 519152174 |
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Institutes: | Medizin |
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Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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Sammlungen: | Universitätspublikationen |
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Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
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