Exploring Intervention Techniques for Alzheimer’s Disease: Conventional Methods and the Role of AI in Advancing Care

Artificial Intelligence and Applications, Год журнала: 2024, Номер unknown

Опубликована: Апрель 7, 2024

Alzheimer’s disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers specific method or approach employed bring about positive change in particular situation. In the context of AD, such are crucial as they aim slow down progression symptoms, alleviate behavioral challenges, support patients their caretakers managing complexities condition. Conventional techniques, stimulation reality orientation, have demonstrated benefits improving function emotional well-being. widely preferred proven track record effectiveness, personalized response, cost-effectiveness, patient-centered care. Despite these benefits, limited individual variability response long-term effectiveness. On other hand, AI-based computer vision deep learning hold potential revolutionize interventions. These technologies offer early detection, care, remote monitoring capabilities. They can provide tailored interventions, assist decision-making, enhance caregiver support. Although interventions face challenges data privacy implementation complexity, transform care significant. research paper approaches. It reveals that while traditional well established novel opportunities for advanced Combining strengths both may lead more comprehensive effective individuals Continued collaboration harness full AI enhancing quality life affected caregivers.

Язык: Английский

---

Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications

Applied Sciences, Год журнала: 2024, Номер 14(22), С. 10121 - 10121

Опубликована: Ноя. 5, 2024

Alzheimer’s disease (AD) is the most common cause of dementia, marked by cognitive decline and memory loss. Recently, machine learning deep techniques have introduced promising solutions for improving AD detection through MRI, especially in settings where specialists may not be readily available. These offer potential to assist general practitioners non-specialists busy clinical environments. However, ‘black box’ nature many AI makes it challenging non-expert physicians fully trust their diagnostic accuracy. In this review, we critically evaluate current explainable (XAI) methods applied highlight limitations. addition, a new interpretability framework, called “Feature-Augmented”, was theoretically designed improve model interpretability. This approach remains underexplored, primarily due scarcity AD-specific datasets. Furthermore, underscore importance models being accurate explainable, which enhance confidence patient care outcomes.

Язык: Английский

---

Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI

Опубликована: Март 4, 2024

Machine Learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, recall may indicate performance of models but not necessarily reliability their outcomes. This paper assesses effectiveness a number machine learning algorithms applied to an important dataset domain, specifically, mental health, by employing explainability methodologies. Using multiple model techniques, project provides insights into workings help determine algorithm predictions. The results are intuitive. It was found that were focusing significantly on less relevant features at times, unsound ranking make therefore argues it for research provide addition other accuracy. particularly applications critical domains such as healthcare. A future direction investigate methods quantify terms from explainability.

Язык: Английский

---

Early detection of dementia using artificial intelligence and multimodal features with a focus on neuroimaging: A systematic literature review

Health and Technology, Год журнала: 2024, Номер 14(2), С. 201 - 237

Опубликована: Фев. 10, 2024

Язык: Английский

---

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

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

---

Advancing Alzheimer's Therapy: Computational Strategies and Treatment Innovations

IBRO Neuroscience Reports, Год журнала: 2025, Номер 18, С. 270 - 282

Опубликована: Фев. 4, 2025

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting need for innovative therapeutic approaches. This review explores challenges AD integration computational methodologies to advance interventions. A comprehensive analysis recent literature was conducted elucidate broad scope etiology limitations drug discovery Our findings underscore critical role models in elucidating mechanisms, identifying targets, expediting discovery. Through simulations, researchers can predict efficacy, optimize lead compounds, facilitate personalized medicine Moreover, machine learning algorithms enhance early diagnosis enable precision analyzing multi-modal datasets. Case studies highlight application techniques therapeutics, including suppression crucial proteins implicated progression repurposing existing drugs management. Computational interplay between oxidative stress neurodegeneration, offering insights into potential Collaborative efforts biologists, pharmacologists, clinicians are essential translate clinically actionable interventions, ultimately improving patient outcomes addressing unmet medical needs individuals affected AD. Overall, integrating represents promising paradigm shift solutions overcome transform landscape treatment.

Язык: Английский

---

Artificial Intelligence for Predicting Progression and Personalizing Healthcare to Alzheimer's Disease Patients

Advances in healthcare information systems and administration book series, Год журнала: 2025, Номер unknown, С. 155 - 190

Опубликована: Янв. 10, 2025

This chapter explains the use of Deep Learning Models from Artificial Intelligence (AI) that take Structural and Functional Magnetic Resonance Imaging (S/FMRI) data to classify Alzheimer's disease (AD) progression stages. Early accurate diagnosis AD is necessary for timely intervention, treatment planning, providing personalized healthcare. Current limitations in diagnostic methods necessitate using AI such as Convolutional Neural Networks (CNN) Recurrent (RNN) extract features MRI develop models predicting Mild Cognitive Impairment (MCI), AD, Dementia. Initial results a case study applied methodology demonstrated improved classification accuracy over traditional accurately classifying stages developing patient care. With more refinement technologies progress, these computational approaches can drastically positively change Healthcare professionals benefit this by understanding how be implemented deal with neurodegenerative diseases.

Язык: Английский

---

Deep learning frameworks for MRI-based diagnosis of neurological disorders: a systematic review and meta-analysis

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 17, 2025

Язык: Английский

---

Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study

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

---

Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations

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

---