Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care

Abadir, Peter; Oh, Esther; Chellappa, Rama; Choudhry, Niteesh; Demiris, George; Ganesan, Deepak; Karlawish, Jason; Marlin, Benjamin; Li, Rose M.; Dehak, Najim; Arbaje, Alicia; Unberath, Mathias; Cudjoe, Thomas; Chute, Christopher; Moore, Jason H.; Phan, Phillip; Samus, Quincy; Schoenborn, Nancy L.; Battle, Alexis; Walston, Jeremy D.

doi:10.1002/alz.13710

Cited by 3 publications

References 22 publications

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The application of artificial intelligence in diagnosis of Alzheimer’s disease: a bibliometric analysis

An,

He,

et al. 2024

Front. Neurol.

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Alzheimer’s disease (AD) is a neurodegenerative disorder that severely impacts cognitive function, posing significant physical and psychological burdens on patients and substantial economic challenges to families and society, particularly in aging populations where its prevalence is rising. Current diagnostic and therapeutic strategies, including pharmacological treatments and non-pharmacological interventions, exhibit considerable limitations in early diagnosis, etiological treatment, and disease management. This study aims to investigate the application of artificial intelligence (AI) in the early diagnosis and progression monitoring of AD through a bibliometric analysis of relevant literature. A systematic search in the Web of Science Core Collection identified 530 publications related to AI and AD, consisting of 361 original research articles and 169 review articles, with a notable increase in annual publication rates, particularly between 2019 and 2024. The United States and China emerged as leading contributors, emphasizing the importance of international collaboration. Institutional analysis revealed that Harvard University and Indiana University System are at the forefront, highlighting the role of academic institutions in fostering interdisciplinary research. Furthermore, the Journal of Alzheimer’s Disease was identified as the most influential publication outlet. Key highly cited papers provided essential theoretical foundations for ongoing research. This study underscores the growing relevance of AI in AD research and suggests promising avenues for future investigations, particularly in enhancing diagnostic accuracy and therapeutic strategies through advanced data analytics and machine learning techniques.

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The application of artificial intelligence in diagnosis of Alzheimer’s disease: a bibliometric analysis

An,

He,

et al. 2024

Front. Neurol.

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

Hasan Saif,

Al-Andoli,

Bejuri

2024

Applied Sciences

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

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Alzheimer's Disease Prediction Using InceptionResNet Integrating Deep Learning Models

Jenath,

Lalitha,

Vidhyalakshmi

et al. 2024

Advances in Bioinformatics and Biomedical Engineering

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This research explores the application of deep learning methodologies for predicting Alzheimer's disease progression using MRI scans and clinical data. The study leverages the InceptionResNet architecture, known for its effectiveness in image classification tasks, to analyze MRI scans from a dataset.Patients diagnosed with Alzheimer's disease. The methodology involves preprocessing MRI images to enhance quality and standardize dimensions, followed by training InceptionResNet on a [mention hardware setup] platform using [mention deep learning framework]. Performance evaluation metrics including accuracy (92%), precision (89%), recall (91%), and F1-score (90%) demonstrate the model's robustness in early-stage disease detection. Comparative analysis with baseline models reveals significant improvements, affirming the efficacy of InceptionResNet in identifying Alzheimer's disease markers. Insights gained from the model contribute to understanding disease progression dynamics, highlighting its potential for clinical application in early diagnosis and intervention.

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Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care

Cited by 3 publications

References 22 publications

The application of artificial intelligence in diagnosis of Alzheimer’s disease: a bibliometric analysis

The application of artificial intelligence in diagnosis of Alzheimer’s disease: a bibliometric analysis

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

Alzheimer's Disease Prediction Using InceptionResNet Integrating Deep Learning Models

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