AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion DOI Creative Commons
Lei Han

Frontiers in Computational Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Март 12, 2025

Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integration the high cost PET image acquisition, which limit their effectiveness in practical applications. To address these issues, this paper proposes an innovative model, AD-Diff. This model significantly improves AD accuracy by integrating images generated through a diffusion process with cognitive scale data other modalities. Specifically, AD-Diff consists two core components: ADdiffusion module Mamba Classifier. The uses 3D generate high-quality images, are then fused MRI tabular provide input for Multimodal Experimental results on OASIS ADNI datasets demonstrate that performs exceptionally well both long-term short-term tasks, reliability. These highlight significant advantages handling complex medical information, providing effective tool early diagnosis personalized disease.

Язык: Английский

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare DOI Creative Commons
Tim Hulsen

AI, Год журнала: 2023, Номер 4(3), С. 652 - 666

Опубликована: Авг. 10, 2023

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, deep learning. can be applied in many different areas, econometrics, biometry, e-commerce, the automotive industry. In recent years, has found its way into healthcare well, helping doctors make better decisions (“clinical decision support”), localizing tumors magnetic resonance images, reading analyzing reports written by radiologists pathologists, much more. However, one big risk: it perceived a “black box”, limiting trust reliability, which is very issue an area mean life or death. As result, term Explainable (XAI) been gaining momentum. XAI tries ensure algorithms (and resulting decisions) understood humans. this narrative review, we will have look at some central concepts XAI, describe several challenges around healthcare, discuss whether really help advance, for example, increasing understanding trust. Finally, alternatives increase discussed, well future research possibilities XAI.

Язык: Английский

Процитировано

110
AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling DOI
Mayur B. Kale, Nitu L. Wankhede,

Rupali S. Pawar

и другие.

Ageing Research Reviews, Год журнала: 2024, Номер unknown, С. 102497 - 102497

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

26
Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings DOI Creative Commons
Heidi Lindroth, Keivan Nalaie, Roshini Raghu

и другие.

Journal of Imaging, Год журнала: 2024, Номер 10(4), С. 81 - 81

Опубликована: Март 28, 2024

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or sequence images to recognize content, has been used extensively across industries in recent years. However, the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV potential improve patient monitoring, system efficiencies, while reducing workload. In contrast previous reviews, we focus on end-user CV. First, briefly review categorize other (job enhancement, surveillance automation, augmented reality). We then developments hospital setting, outpatient, community settings. The advances monitoring delirium, pain sedation, deterioration, mechanical ventilation, mobility, surgical applications, quantification workload hospital, for events outside highlighted. To identify opportunities future also completed journey mapping at different levels. Lastly, discuss considerations associated with outline processes algorithm development testing limit expansion healthcare. This comprehensive highlights ideas expanded use

Язык: Английский

Процитировано

23
A comprehensive review on early detection of Alzheimer's disease using various deep learning techniques DOI Creative Commons

I. Nagarajan,

G. G. Lakshmi Priya

Frontiers in Computer Science, Год журнала: 2025, Номер 6

Опубликована: Янв. 10, 2025

Alzheimer's disease (AD) is a type of brain that makes it hard for someone to perform daily tasks. Early diagnosis and classification the condition are thought be essential study areas due speedy progression in people living with dementia absence precise diagnostic procedures. One main aims researchers correctly identify early stages AD so can prevented or significantly reduced. The objective current review thoroughly examine most recent work on detection using deep learning (DL) approach. This paper examined purpose an AD, various neuroimaging modalities, pre-processing methods were employed, maintenance data, used classifying from magnetic resonance imaging (MRI) images, publicly available datasets, data fed into models. A comparative analysis different DL techniques performed. Further, discussed challenges involved detection.

Язык: Английский

Процитировано

2
Machine-learning models for Alzheimer’s disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation DOI Creative Commons
Maryam Akhavan Aghdam, Serdar Bozdag, Fahad Saeed

и другие.

Brain Informatics, Год журнала: 2025, Номер 12(1)

Опубликована: Март 21, 2025

Abstract Clinical diagnosis of Alzheimer’s disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between stable MCI (sMCI) cases (i.e., patients who do not convert to AD for at least three years) progressive (pMCI) in years or sooner). Delayed also disproportionately affects underrepresented socioeconomically disadvantaged populations. significant positive impact an early solution across diverse ethno-racial demographic groups well-known recognized. While advancements high-throughput technologies have enabled generation vast amounts multimodal clinical, neuroimaging datasets related AD, most methods utilizing these data sets diagnostic purposes found their way clinical settings. To better understand landscape, we surveyed major preprocessing, management, traditional machine-learning (ML), deep learning (DL) used diagnosing using structural magnetic resonance imaging (sMRI), functional (fMRI), positron emission tomography (PET). Once had a good understanding available, conducted study assess reproducibility generalizability open-source ML models. Our evaluation shows that models show reduced when different cohorts modality while controlling other computational factors. paper concludes with discussion challenges plague biomarker discovery.

Язык: Английский

Процитировано

2
Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease? DOI Creative Commons
Sophia Mirkin, Benedict C. Albensi

Frontiers in Aging Neuroscience, Год журнала: 2023, Номер 15

Опубликована: Апрель 18, 2023

Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there no cure, detecting AD early important for the development of therapeutic plan care may preserve function prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission (PET), has served critical tool in establishing diagnostic indicators during preclinical stage. However, neuroimaging technology quickly advances, challenge analyzing interpreting vast amounts brain data. Given these limitations, great interest using artificial Intelligence (AI) to assist this process. AI introduces limitless possibilities future diagnosis AD, yet still resistance from healthcare community incorporate clinical setting. The goal review answer question whether should be used conjunction with AD. To question, possible benefits disadvantages are discussed. main advantages its potential improve accuracy, efficiency radiographic data, reduce physician burnout, advance precision medicine. include generalization data shortage, lack vivo gold standard, skepticism medical community, bias, concerns over patient information, privacy, safety. challenges present fundamental must addressed when time comes, it would unethical not use if can health outcome.

Язык: Английский

Процитировано

30
Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection DOI Creative Commons
Wei Wang, Jian Chen, Yuzhu Hu

и другие.

International Journal of Cognitive Computing in Engineering, Год журнала: 2024, Номер 5, С. 307 - 315

Опубликована: Янв. 1, 2024

The integration of Artificial Intelligence (AI) and Wearable Internet Things (WIoT) for mental health detection is a promising area research with the potential to revolutionize monitoring diagnosis. Since early diseases, i.e., depression, great importance diagnosis treatment, fast convenient way urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, easily lead misdiagnosis. advance in information techniques wearable devices brings innovation disease detection. Therefore, this article first compares intelligent depression traditional illustrate significance then analyzes opportunities device. Then we provide specific psychophysiological data measured by introduce relevant datasets An illustrative example sleep presented discussed our proposed ensemble method has improved nearly 10% baselines. Analytical results demonstrate using device-measured detect intelligently.

Язык: Английский

Процитировано

11
Deep learning methods for early detection of Alzheimer’s disease using structural MR images: a survey DOI
Sonia Ben Hassen, Mohamed Néji, Zain Hussain

и другие.

Neurocomputing, Год журнала: 2024, Номер 576, С. 127325 - 127325

Опубликована: Янв. 23, 2024

Язык: Английский

Процитировано

10
Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images DOI
Madhusudan G. Lanjewar, Jivan S. Parab,

Arman Yusuf Shaikh

и другие.

Multimedia Tools and Applications, Год журнала: 2022, Номер 82(8), С. 12699 - 12717

Опубликована: Сен. 26, 2022

Язык: Английский

Процитировано

35
A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer’s disease using neuroimaging DOI

Xinze Xu,

Lan Lin,

Shen Sun

и другие.

Reviews in the Neurosciences, Год журнала: 2023, Номер 34(6), С. 649 - 670

Опубликована: Фев. 2, 2023

Abstract Alzheimer’s disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis detect AD at early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they emerged as promising research direction in the of AD. The aim this article present current state art CNN models modalities, focusing architectures classification methods used, highlight potential future topics. give reader better overview content mentioned review, we briefly introduce commonly used imaging datasets fundamentals architectures. Then carefully analyzed existing studies diagnosis, which are divided into two levels according their inputs: subject-level patch-level highlighting contributions significance field. In addition, review discusses key findings challenges from highlights lessons learned roadmap for research. Finally, summarize paper by presenting some major findings, identifying open challenges, pointing out directions.

Язык: Английский

Процитировано

21