WIMOAD: Weighted Integration of Multi-omics data for Alzheimer's Disease (AD) Diagnosis DOI Creative Commons

Hanyu Xiao,

Jieqiong Wang, Shibiao Wan

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 27, 2024

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially memory, thinking, and reasoning ability. Early diagnosis interventions enable implementation measures to reduce or slow further regression disease, preventing individuals from severe brain function decline. The current framework AD depends on A/T/(N) biomarkers detection cerebrospinal fluid imaging data, which invasive expensive during data acquisition process. Moreover, pathophysiological changes accumulate amino acids, metabolism, neuroinflammation, etc., resulting heterogeneity newly registered patients. Recently, next generation sequencing (NGS) technologies have found be non-invasive, efficient less-costly alternative screening. However, existing studies rely single omics only. To address these concerns, we introduce WIMOAD, weighted integration multi-omics for diagnosis. WIMOAD synergistically leverages specialized classifiers patients' paired gene expression methylation multi-stage classification. scores were then stacked with MLP-based meta-models performance improvement. prediction results two distinct integrated optimized weights final decision-making model, providing higher than using Remarkably, achieves significantly alone classification tasks. model's overall also outperformed approaches, highlighting its ability effectively discern intricate patterns their correlations clinical results. In addition, stands out as biologically interpretable model leveraging SHapley Additive exPlanations (SHAP) elucidate contributions each output. We believe very promising tool accurate effective biomarker discovery across different progression stages, eventually will consequential impacts early treatment intervention personalized therapy design AD.

Язык: Английский

A review of deep learning-based information fusion techniques for multimodal medical image classification DOI Creative Commons
Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 177, С. 108635 - 108635

Опубликована: Май 22, 2024

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various modalities to provide more comprehensive understanding of the underlying pathology. Recently, deep learning-based multimodal fusion techniques have emerged powerful tools for improving image classification. This review offers thorough analysis developments classification tasks. We explore complementary relationships among prevalent outline three main schemes networks: input fusion, intermediate (encompassing single-level hierarchical attention-based fusion), output fusion. By evaluating performance these techniques, we insight into suitability different network architectures scenarios application domains. Furthermore, delve challenges related architecture selection, handling incomplete data management, potential limitations Finally, spotlight promising future Transformer-based give recommendations research this rapidly evolving field.

Язык: Английский

Процитировано

29
Alzheimer’s disease unveiled: Cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis DOI
Tariq Mahmood, Amjad Rehman, Tanzila Saba

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 97, С. 106721 - 106721

Опубликована: Авг. 8, 2024

Язык: Английский

Процитировано

8
MACFNet: Detection of Alzheimer's disease via multiscale attention and cross-enhancement fusion network DOI Creative Commons
Chaosheng Tang, Mengbo Xi, Junding Sun

и другие.

Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 254, С. 108259 - 108259

Опубликована: Июнь 6, 2024

Alzheimer's disease (AD) is a dreaded degenerative that results in profound decline human cognition and memory. Due to its intricate pathogenesis the lack of effective therapeutic interventions, early diagnosis plays paramount role AD. Recent research based on neuroimaging has shown application deep learning methods by multimodal neural images can effectively detect However, these only concatenate fuse high-level features extracted from different modalities, ignoring fusion interaction low-level across modalities. It consequently leads unsatisfactory classification performance. In this paper, we propose novel multi-scale attention cross-enhanced network, MACFNet, which enables multi-stage between inputs learn shared feature representations. We first construct Cross-Enhanced Fusion Module (CEFM), fuses modalities through cross-structure. addition, an Efficient Spatial Channel Attention (ECSA) module proposed, able focus important AD-related more efficiently achieve enhancement two-stage residual concatenation. Finally, also multiscale guiding block (MSAG) dilated convolution, obtain rich receptive fields without increasing model parameters computation, improve efficiency extraction. Experiments Disease Neuroimaging Initiative (ADNI) dataset demonstrate our MACFNet better performance than existing methods, with accuracies 99.59%, 98.85%, 99.61%, 98.23% for AD vs. CN, MCI, CN MCI respectively, specificity 98.92%, 97.07%, 99.58% 99.04%, sensitivity 99.91%, 99.89%, 99.63% 97.75%, respectively. The proposed high-accuracy diagnostic framework. Through cross mechanism efficient attention, make full use modal medical pay local global information images. This work provides valuable reference multi-mode diagnosis.

Язык: Английский

Процитировано

4
Assessing the Interpretability of Machine Learning Models in Early Detection of Alzheimer's Disease DOI

Karim Haddada,

Mohamed Ibn Khedher, Olfa Jemai

и другие.

Опубликована: Июль 8, 2024

Alzheimer's disease (AD) is a chronic and irreversible neurological disorder, making early detection essential for managing its progression.This study investigates the coherence of SHAP values with medical scientific truth.It examines three types features: clinical, demographic, FreeSurfer extracted from MRI scans.A set six ML classifiers are investigated their interpretability levels.This validated on OASIS-3 dataset binary classification.The results show that clinical data outperforms others, margin 14% over features, second-best features.In case explanations provided by tree-based consistently align insights.This comparison was calculated using Kendall Tau distance.

Язык: Английский

Процитировано

3
ViT transfer learning for fMRI (VTFF): A highway to achieve superior performance for multi-classification of cognitive decline DOI
Bocheng Wang

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107557 - 107557

Опубликована: Янв. 24, 2025

Язык: Английский

Процитировано

0
Integrating imaging and genetic data via wavelet transform-based CNN for Alzheimer ’s disease classification DOI
Jinwang Feng, Mingfeng Jiang, Haowen Zhang

и другие.

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107583 - 107583

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

0
Deep Feature Fusion Framework for Alzheimer’s Disease Staging Using Neuroimaging Modalities DOI
Aya Gamal, Mustafa Elattar, Sahar Selim

и другие.

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 277 - 288

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Optimized stacked long short-term memory with hyperbolic secant activation function for Alzheimer’s disease classification DOI

Krishna Kishore Maaram,

Shanker Chandre

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 108, С. 107980 - 107980

Опубликована: Апрель 29, 2025

Язык: Английский

Процитировано

0
Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review DOI Creative Commons
Zia‐ur‐Rehman, Mohd Khalid Awang, Ghulam Ali

и другие.

Health Science Reports, Год журнала: 2025, Номер 8(5)

Опубликована: Май 1, 2025

ABSTRACT Purpose Alzheimer's disease (AD) is a severe neurological that significantly impairs brain function. Timely identification of AD essential for appropriate treatment and care. This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging diagnosis, where popular imaging types, reviews well‐known online accessible data sets, describes different algorithms used DL the correct initial evaluation are presented. Significance Conventional diagnostic techniques, including medical evaluations cognitive assessments, usually not identify stages Alzheimer's. Neuroimaging methods, when integrated have demonstrated considerable potential enhancing diagnosis categorization AD. models received significant interest due their capability its early phases automatically, which reduces mortality rate cost Method An extensive literature search was performed leading scientific databases, concentrating on papers published from 2021 2025. Research leveraging techniques such as magnetic resonance (MRI), positron emission tomography, functional (fMRI), so forth. The complies Preferred Reporting Items Systematic Reviews Meta‐Analyses (PRISMA) guidelines. Results Current show CNN‐based especially those utilizing hybrid transfer frameworks, outperform conventional methods. employing combination multimodal has enhanced precision. Still, challenges method interpretability, heterogeneity, limited exist issues. Conclusion considerably improved accuracy reliability neuroimaging. Regardless issues accessibility adaptability, studies into interpretability fusion provide clinical application. Further research should concentrate standardized rigorous validation architectures, understandable AI methodologies enhance effectiveness methods prediction.

Язык: Английский

Процитировано

0
A survey of early detection and interpretable diagnosis of Alzheimer’s disease DOI

Karim Haddada,

Mohamed Ibn Khedher, Olfa Jemai

и другие.

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Май 8, 2025

Язык: Английский

Процитировано

0