Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations DOI Creative Commons
Jamel Baili, Abdullah Alqahtani, Ahmad Almadhor

и другие.

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Апрель 1, 2025

Alzheimer's disease (AD) and Parkinson's (PD) are two of the most prevalent neurodegenerative disorders, necessitating accurate diagnostic approaches for early detection effective management. This study introduces deep learning architectures, Residual-based Attention Convolutional Neural Network (RbACNN) Inverted (IRbACNN), designed to enhance medical image classification AD PD diagnosis. By integrating self-attention mechanisms, these models improve feature extraction, interpretability, address limitations traditional methods. Additionally, explainable AI (XAI) techniques incorporated provide model transparency clinical trust in automated diagnoses. Preprocessing steps such as histogram equalization batch creation applied optimize quality balance dataset. The proposed achieved an outstanding accuracy 99.92%. results demonstrate that combination with XAI, facilitate precise diagnosis, thereby contributing reducing global burden diseases.

Язык: Английский

The Role of Genetic, Environmental, and Dietary Factors in Alzheimer’s Disease: A Narrative Review DOI Open Access
Beyza Mertaş, İffet İpek Boşgelmez

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(3), С. 1222 - 1222

Опубликована: Янв. 30, 2025

Alzheimer’s disease (AD) is one of the most common and severe forms dementia neurodegenerative disease. As life expectancy increases in line with developments medicine, elderly population projected to increase next few decades; therefore, an prevalence some diseases, such as AD, also expected. a result, until radical treatment becomes available, AD expected be more frequently recorded top causes death worldwide. Given current lack cure for only treatments available being ones that alleviate major symptoms, identification contributing factors influence incidence crucial. In this context, genetic and/or epigenetic factors, mainly environmental, disease-related, dietary, or combinations/interactions these are assessed. review, we conducted literature search focusing on environmental air pollution, toxic elements, pesticides, infectious agents, well dietary including various diets, vitamin D deficiency, social (e.g., tobacco alcohol use), variables affected by both behavior gut microbiota. We evaluated studies beneficial effects antibiotics Mediterranean-DASH Intervention Neurodegenerative Delay (MIND) Mediterranean diets.

Язык: Английский

Процитировано

0
Harnessing Artificial Intelligence in Early Detection and Diagnosis of Alzheimer's Disease: Current and Future Applications DOI

Alaa Abdelfattah,

Waseem Sajjad, Imtiaz Ali Soomro

и другие.

Indus journal of bioscience research., Год журнала: 2025, Номер 3(2), С. 199 - 212

Опубликована: Фев. 25, 2025

Alzheimer's Disease (AD) is a neurodegenerative disorder requiring early detection. This study compares AI models—Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest (RF)—in analyzing neuroimaging data (MRI, PET) to enhance AD prediction improve diagnosis using machine learning techniques. Through the application of multi-modal in form genetic, clinical, data, also investigates effectiveness combining different types predictability models for diagnosis. Feature importance analysis was performed methods like SHAP (SHAP (Shapley Additive Explanations) values determine most important variables model predictions, e.g., certain brain regions or genetic components. The generalizability real-world applicability by training on an independent dataset representing diverse clinical settings. performance each assessed variety statistical measures accuracy, precision, recall, F1-score, Area Under Curve (AUC). findings showed that CNN better compared SVM RF all metrics with highest accuracy (92%), precision (93%), recall (91%), AUC (0.95). suggest effectively detects subtle patterns, making it strong tool While well, superior accuracy. Cross-validation confirmed its generalizability, crucial use. Implementing models, especially CNN, may enable earlier detection, timely interventions, improved patient outcomes Alzheimer’s care. References

Язык: Английский

Процитировано

0
Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study DOI Creative Commons
Patrizia Ribino, Claudia Di Napoli, Giovanni Paragliola

и другие.

BioData Mining, Год журнала: 2025, Номер 18(1)

Опубликована: Март 28, 2025

Dementia due to Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by various cognitive and behavioral decline factors. In this work, we propose an extension of the traditional k-means clustering for multivariate time series data cluster joint trajectories different features describing progression over time. The algorithm here enables analysis longitudinal explore co-occurring trajectory factors among markers indicative in individuals participating AD study. By examining how multiple variables co-vary evolve together, identify distinct subgroups within cohort based on their trajectories. Our method enhances understanding individual development across dimensions provides deeper medical insights into decline. addition, proposed also able make selection most significant separating clusters considering This process, together with preliminary pre-processing OASIS-3 dataset, reveals important role some neuropsychological particular, has identified profile compatible syndrome known as Mild Behavioral Impairment (MBI), displaying manifestations that may precede symptoms typically observed patients. findings underscore importance clinical modeling, ultimately supporting more effective individualized patient management strategies.

Язык: Английский

Процитировано

0
Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations DOI Creative Commons
Jamel Baili, Abdullah Alqahtani, Ahmad Almadhor

и другие.

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Апрель 1, 2025

Alzheimer's disease (AD) and Parkinson's (PD) are two of the most prevalent neurodegenerative disorders, necessitating accurate diagnostic approaches for early detection effective management. This study introduces deep learning architectures, Residual-based Attention Convolutional Neural Network (RbACNN) Inverted (IRbACNN), designed to enhance medical image classification AD PD diagnosis. By integrating self-attention mechanisms, these models improve feature extraction, interpretability, address limitations traditional methods. Additionally, explainable AI (XAI) techniques incorporated provide model transparency clinical trust in automated diagnoses. Preprocessing steps such as histogram equalization batch creation applied optimize quality balance dataset. The proposed achieved an outstanding accuracy 99.92%. results demonstrate that combination with XAI, facilitate precise diagnosis, thereby contributing reducing global burden diseases.

Язык: Английский

Процитировано

0