ArticleSensors (Basel, Switzerland)2021
Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer's Disease: A Preprocessing-Focused Pipeline.Journal of imaging · 2026Article
- No evidence for modulation of frontal brain activity asymmetry by a single session of EEG feedback.Neuroimage. Reports · 2026Article
- EEG hemispheric asymmetry and behavioral responsiveness following rTMS in unresponsive wakefulness syndrome: an exploratory study.Journal of neuroengineering and rehabilitation · 2026Article
- Sequence-robust MRI asymmetry measures for the detection of anoperineal lesions in Crohn's disease on axial pelvic MRI scans.PloS one · 2026Article
- Hippocampal texture asymmetry features onFrontiers in immunology · 2026Article
- The Cortical Asymmetry Index for subtyping dementia patients.European radiology · 2025Article
- Socioeconomic Differences in Global Brain Asymmetry: An Integrative Approach Using Image Similarity Measures and Structural Equation Modeling.Research square · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Article
- A multiscale characterization of cortical shape asymmetries in early psychosis.Brain communications · 2024Article
- Diagnostic challenge of Creutzfeldt-Jakob disease in a patient with multimorbidity: a case-report.BMC neurology · 2023Article
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm.Scientific reports · 2022Article
- Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.Rheumatology and therapy · 2022Article
- Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.Bioengineering (Basel, Switzerland) · 2022Review
- Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.Human brain mapping · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer's Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.