Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography

Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 1, 2025

Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed develop a deep-learning algorithm using OCT AD MCI. performed cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development testing on 68 (52 cases/16 85 White (39 cases/46 participants. Features from were used an ensemble trilateral model. The significantly outperformed single non-deep learning models in (area under the curve [AUC] = 0.91 vs. 0.71-0.72, p 0.022-0.032) (AUC 0.84 0.58-0.75, 0.056- < 0.001) populations. However, its was comparable that statistical (AUCs similar, > 0.05). Both multimodal approaches, deep or traditional models, show promise MCI detection. choice between these may depend computational resources, interpretability preferences, clinical needs. A developed images.The combined parameters both cohorts.The demonstrates potential OCT-based algorithms

Language: Английский

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Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease

Diagnostics, Journal Year: 2025, Volume and Issue: 15(5), P. 612 - 612

Published: March 4, 2025

Alzheimer's disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly deep learning machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity lack of interpretability these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, fostering trust AI-driven diagnostics. This review explores role XAI neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, Layer-wise Relevance Propagation (LRP). We examine applications identifying critical biomarkers, tracking progression, distinguishing stages using various imaging modalities, including MRI PET. Additionally, we discuss current challenges, dataset limitations, regulatory concerns, standardization issues, propose future research directions to improve XAI's integration practice. By bridging gap between AI interpretability, holds potential refine diagnostics, personalize treatment strategies, advance research.

Language: Английский

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High-resolution fundus images for ophthalmomics and early cardiovascular disease prediction

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 3, 2025

Cardiovascular diseases (CVDs) remain the foremost cause of mortality globally, emphasizing imperative for early detection to improve patient outcomes and mitigate healthcare burdens. Carotid intima-media thickness (CIMT) serves as a well-established predictive marker atherosclerosis cardiovascular risk assessment. Fundus imaging offers non-invasive modality investigate microvascular pathology systemic vascular health. However, paucity high-quality, publicly available datasets linking fundus images with CIMT measurements has hindered progression AI-driven models CVDs. Addressing this gap, we introduce China-Fundus-CIMT dataset, comprising bilateral high-resolution images, measurements, clinical data-including age gender-from 2,903 patients. Our experiments multimodal reveal that integrating information substantially enhances performance, yielding AUC-ROC increases 3.22% 7.83% on validation test sets, respectively, compared unimodal models. This dataset constitutes vital resource developing validating AI-based screening CVDs using is now accessible research community.

Language: Английский

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

Signals, Journal Year: 2024, Volume and Issue: 5(2), P. 343 - 381

Published: May 31, 2024

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

Language: Английский

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

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Oct. 9, 2024

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

Language: Английский

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

Cancers, Journal Year: 2024, Volume and Issue: 16(11), P. 2138 - 2138

Published: June 4, 2024

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

Language: Английский

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Artificial intelligence in stroke risk assessment and management via retinal imaging

Frontiers in Computational Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 17, 2025

Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning deep algorithms, showing promise in early disease detection, severity grading, prognostic evaluation stroke patients. This review explores the role of artificial intelligence (AI) patient care, focusing on imaging integration into clinical workflows. has revealed several microvascular including decrease central artery diameter an increase vein diameter, both which are associated with lacunar intracranial hemorrhage. Additionally, such as arteriovenous nicking, increased vessel tortuosity, arteriolar light reflex, decreased fractals, thinning nerve fiber layer also reported to higher risk. AI models, Xception EfficientNet, have demonstrated accuracy comparable traditional risk scoring systems predicting For diagnosis, models like Inception, ResNet, VGG, alongside classifiers, shown high efficacy distinguishing patients from healthy individuals using imaging. Moreover, random forest model effectively distinguished between ischemic hemorrhagic subtypes based features, superior predictive performance compared characteristics. support vector achieved classification pial collateral status. Despite this advancements, challenges lack standardized protocols modalities, hesitance trusting AI-generated predictions, insufficient data electronic health records, need validation across diverse populations, ethical regulatory concerns persist. Future efforts must focus validating ensuring algorithm transparency, addressing issues enable broader implementation. Overcoming these barriers will essential translating technology personalized care improving outcomes.

Language: Английский

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

Advances in healthcare information systems and administration book series, Journal Year: 2025, Volume and Issue: unknown, P. 155 - 190

Published: Jan. 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.

Language: Английский

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The connection between Alzheimer's disease and retinal changes: how eye scans can help with early diagnosis

Nursing and Residential Care, Journal Year: 2025, Volume and Issue: 27(2), P. 1 - 3

Published: Feb. 2, 2025

Sarah Jane Palmer discusses the novel approach of using eye scans to identify early signs neurodegenerative diseases.

Language: Английский

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Current and future roles of artificial intelligence in retinopathy of prematurity

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)

Published: March 29, 2025

Abstract Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, detachment, and potential blindness. While semi-automated systems have been used in the past diagnose ROP-related plus disease by quantifying features, traditional machine learning (ML) models face challenges like accuracy overfitting. Recent advancements deep (DL), especially convolutional neural networks (CNNs), significantly improved ROP detection classification. The i-ROP (i-ROP-DL) system also shows promise detecting disease, offering reliable diagnosis potential. This research comprehensively examines contemporary progress associated with using imaging artificial intelligence (AI) detect ROP, valuable insights that can guide further investigation this domain. Based on 84 original studies field (out 2025 were reviewed), we concluded methods for suffer from subjectivity manual analysis, inconsistent clinical decisions. AI holds great improving management. review explores AI’s detection, classification, diagnosis, prognosis.

Language: Английский

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