Retinal imaging and Alzheimer’s disease: a future powered by Artificial Intelligence

Ashayeri, Hamidreza; Jafarizadeh, Ali; Yousefi, Milad; Farhadi, Fereshteh; Javadzadeh, Alireza

doi:10.1007/s00417-024-06394-0

Cited by 6 publications

(3 citation statements)

References 66 publications

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“…The band δ (0.5 ÷ 4 Hz) signals slow brain activity linked to cortical damage, θ (4 ÷ 8 Hz) indicates transitions between sleep and wakefulness suggesting potential dysfunctions, α (8 ÷ 12 Hz) is associated with resting states and reflects the alteration of brain organization in AD, and β (12 ÷ 30 Hz) highlights levels of attention and mental activity, which is useful for observing cognitive changes in the patient [32,33]. Finally, the γ rhythm, above 30 Hz, is associated with complex cognitive processes such as object recognition and meaning attribution, and it is mainly detectable in the frontal regions [32][33][34][35][36][37]. Detailed EEG analysis, which includes the observation of specific changes in frequency bands, helps define a neurophysiological profile of AD [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Versaci,

La Foresta

2024

Signals

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Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer’s disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics.

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Section: Introductionmentioning

confidence: 99%

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Versaci,

La Foresta

2024

Signals

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“…Additionally, processing labeled data by AI requires more time than unlabeled data. The need for an expert, the labeling process, and the data processing in the training phase are all significant challenges in AI model development for predictive tasks [ 9 , 10 , 11 ]. However, methods like transfer learning (TL) have been developed to address these challenges [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…The need for an expert, the labeling process, and the data processing in the training phase are all significant challenges in AI model development for predictive tasks [ 9 , 10 , 11 ]. However, methods like transfer learning (TL) have been developed to address these challenges [ 11 ]. TL uses an ML model that has been pre-trained in one task (named the source domain), which is then related to the current task (called the target domain).…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Ashayeri,

Sobhi,

Pławiak

et al. 2024

Cancers

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Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

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Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade

Abdollahi,

Jafarizadeh,

Ghafouri‐Asbagh

et al. 2024

WIREs Data Min & Knowl

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Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for the analysis of different CVD‐related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with noninvasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI‐assisted early detection and prediction of cardiovascular diseases on a large scale. A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and AI. The study included 87 English‐language publications selected for relevance, and additional references were considered. This article provides an overview of the recent developments and difficulties in using AI and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and DL are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence

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Retinal imaging and Alzheimer’s disease: a future powered by Artificial Intelligence

Cited by 6 publications

References 66 publications

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade

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