ArticleFrontiers in aging neuroscience2022
A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.Aging clinical and experimental research · 2023Pooled it
- Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.BMC medical imaging · 2026Article
- Targeting neuroplasticity in old brain: restoring synapse with cognitive strategies.Biogerontology · 2026Review
- Segmentation-Guided Development of Visual Classification Criteria for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Gut microbiota and the tryptophan-kynurenine pathway in anxiety: new insights and treatment strategies.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- Eye movement abnormalities in normal pressure hydrocephalus: a video-oculographic study.Journal of neurology · 2025Article
- LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.Physical and engineering sciences in medicine · 2024Article
- Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks.IEEE journal of biomedical and health informatics · 2024Article
- Advancing Alzheimer's research: Radiomics visualization of the default mode network in cerebral perfusion imaging.Journal of applied clinical medical physics · 2024Article
- Current and future perspectives of an early diagnosis of cognitive impairment.Frontiers in neurology · 2023Article
- Magnetic resonance radiomics-based deep learning model for diagnosis of Alzheimer's disease.Digital healthArticle
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7 authors.
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Abstract
Introduction: Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning. Methods: In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve. Results: Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification. Discussion: The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
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