ArticleFrontiers in aging neuroscience2020
Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning.
Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Amyloid-beta statuses prediction with free water MR imaging features in Alzheimer's disease using machine learning models.BMC medical imaging · 2026Article
- Early Alzheimer's risk detection via diffusion tensor imaging using a few-shot multichannel attention residual learning network.Frontiers in artificial intelligence · 2026Article
- The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.International journal of legal medicine · 2025Article
- Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment.Medical & biological engineering & computing · 2025Article
- Gadolinium based contrast agent induced electrical conductivity heterogeneity analysis in the brain of Alzheimer's disease.Scientific reports · 2025Article
- InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification.Frontiers in artificial intelligence · 2025Article
- Brain functional connectivity analysis of fMRI-based Alzheimer's disease data.Frontiers in medicine · 2025Article
- Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.Cognitive neurodynamics · 2024Review
- A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.Neuroinformatics · 2024Article
- Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.Scientific reports · 2023Article
- Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.European radiology · 2023Article
- Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features.Aging clinical and experimental research · 2023Article
- CsAGP: Detecting Alzheimer's disease from multimodal images via dual-transformer with cross-attention and graph pooling.Journal of King Saud University. Computer and information sciences · 2023Article
- AHANet: Adaptive Hybrid Attention Network for Alzheimer's Disease Classification Using Brain Magnetic Resonance Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- Autism spectrum disorder recognition based on multi-view ensemble learning with multi-site fMRI.Cognitive neurodynamics · 2023Article
- Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging.Frontiers in neuroscience · 2023Article
- Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data.Scientific reports · 2022Article
- Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.Frontiers in aging neuroscience · 2022Article
- A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification.Frontiers in aging neuroscience · 2022Article
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4 authors.
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Abstract
Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely important to diagnose MCI as early as possible, as this makes it possible to delay its progression toward AD. However, it's challenging to identify early MCI (EMCI) because there are only mild changes in the brain structures of patients compared with a normal control (NC). To extract remarkable features for these mild changes, in this paper, a multi-modality diagnosis approach based on deep learning is presented. Firstly, we propose to use structure MRI and diffusion tensor imaging (DTI) images as the multi-modality data to identify EMCI. Then, a convolutional neural network based on transfer learning technique is developed to extract features of the multi-modality data, where an L1-norm is introduced to reduce the feature dimensionality and retrieve essential features for the identification. At last, the classifier produces 94.2% accuracy for EMCI vs. NC on an ADNI dataset. Experimental results show that multi-modality data can provide more useful information to distinguish EMCI from NC compared with single modality data, and the proposed method can improve classification performance, which is beneficial to early intervention of AD. In addition, it is found that DTI image can act as an important biomarker for EMCI from the point of view of a clinical diagnosis.
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