ArticleAlzheimer's research & therapy2022
Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes.
Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Identification of Key Osteoarthritis-Associated Genes Based on DNA Methylation.International journal of molecular sciences · 2026Article
- Comprehensive and Region-Specific Retinal Health Assessment Using Phasor Analysis of Multispectral Images and Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Evaluating the correspondence of neuropsychological profiles with clinical diagnostic criteria for frontotemporal lobar degeneration and Alzheimer's disease: insights fromFrontiers in dementia · 2026Article
- Unravelling lumbar disc herniation severity beyond MRI : integrated transcriptomic and metabolomic analyses highlight glycerophospholipid metabolism and inform a machine-learning diagnostic model: a pilot study.Bone & joint research · 2025Article
- Opaque ontology: neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank.GigaScience · 2025Article
- Development of sensor system and data analytic framework for non-invasive blood glucose prediction.Scientific reports · 2024Article
- Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank.bioRxiv : the preprint server for biology · 2024Article
- Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.Frontiers in neuroscience · 2024Article
- Explainable machine learning radiomics model for Primary Progressive Aphasia classification.Frontiers in systems neuroscience · 2024Article
- Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.Diagnostics (Basel, Switzerland) · 2023Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial neural networks for non-linear age correction of diffusion metrics in the brain.Frontiers in aging neuroscience · 2022Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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30 authors.
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Abstract
importanceThe 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.
objectiveMulti-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
participantsAtlas-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.
interventionsN.A. MAIN OUTCOMES AND MEASURES: Cohen's kappa, accuracy, and F1-score to assess model performance.
resultsOverall, 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.
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