Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

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

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A Logistic Regression and Decision Tree Based Hybrid Approach to Predict Alzheimer's Disease

2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Год журнала: 2023, Номер unknown, С. 722 - 726

Опубликована: Апрель 28, 2023

Alzheimer's disease is a degenerative neurological disorder that typically impacts individuals over the age of 65, causing damage to brain and resulting in challenges with memory, cognition, behavior. Although there currently no cure for this condition, various therapies medications exist manage symptoms slow progression disease. Individuals experience cognitive impairment, changes behavior, communication difficulties, often leading psychological behavioral issues like anxiety, aggression, depression. In research paper, novel approach predicting outcomes using MRI data sets from Open Access Series Imaging Studies (OASIS) proposed. The method involves exploration, preprocessing, development hybrid model utilizes both Logistic Regression Decision Tree algorithms. Through rigorous testing, proposed demonstrated superior accuracy compared existing models, achieving an overall rate 96%.

Язык: Английский

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A machine learning model for Alzheimer's disease prediction

IET Cyber-Physical Systems Theory & Applications, Год журнала: 2024, Номер 9(2), С. 125 - 134

Опубликована: Март 20, 2024

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health has no cure, its early diagnosis can help to reduce impacts. A methodology SMOTE‐RF proposed for AD prediction. Alzheimer's predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) evaluated in Open Access Series Imaging Studies longitudinal dataset available on Kaggle used experiments. The balanced synthetic minority oversampling technique. Experiments done both imbalanced datasets. Decision tree obtained 73.38% accuracy, XGB 83.88% accuracy RF maximum 87.84% the dataset. 83.15% 91.05% 95.03% achieved with SMOTE‐RF.

Язык: Английский

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A Novel Light-Weight Convolutional Neural Network Model to Predict Alzheimer’s Disease Applying Weighted Loss Function

Deleted Journal, Год журнала: 2024, Номер 3(4)

Опубликована: Апрель 18, 2024

Alzheimer’s disease (AD) is a progressive neurological disorder that presents significant public health concern. Early detection of has the potential to greatly improve patient care and treatment. Artificial intelligence (AI) revolutionize healthcare by improving outcomes empowering providers. In recent years, breakthroughs in medical diagnosis have occurred, thanks use AI, particularly through application deep learning (DL) techniques. These advancements outcomes. Several proposals been developed utilizing DL techniques identify AD. This study proposes model classify individuals with AD using magnetic resonance imaging images. The aims evaluate DL’s effectiveness predicting proposed used custom-weighted loss function, resulting 99.24% training accuracy, 96.95% test Cohen’s kappa score 0.931, weighted average precision 97%. evaluated against several pre-trained models. Regarding accuracy findings score, suggested performs better than others.

Язык: Английский

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Harnessing the potentials of machine learning models in Alzheimer's disease prediction and detection

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 259 - 267

Опубликована: Янв. 1, 2025

Язык: Английский

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Ensemble Model for Diagnostic Classification of Alzheimer’s Disease Based on Brain Anatomical Magnetic Resonance Imaging

Diagnostics, Год журнала: 2022, Номер 12(12), С. 3193 - 3193

Опубликована: Дек. 16, 2022

Alzheimer’s is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about brain’s anatomy and enables identification diagnostic features. Artificial intelligence (AI) in neuroimaging has potential significantly enhance treatment process for disease (AD). The objective this study two-fold: (1) compare existing Machine Learning (ML) algorithms classification AD. (2) To propose an effective ensemble-based model same perform its comparative analysis. In study, data from Diseases Initiative (ADNI), online repository, utilized experimentation consisting 2125 neuroimages (n = 975), mild cognitive impairment 538) normal 612). For classification, framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbor (K-NN) followed by some variations Support Vector (SVM), such as SVM (RBF kernel), (Polynomial Kernel), (Sigmoid well Gradient Boost (GB), Extreme Boosting (XGB) Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, Ensemble Based Generic Kernel presented where Master-Slave architecture combined attain better performance. proposed ensemble Boosting, SVM_Polynomial kernel (XGB + DT SVM). At last, method evaluated using cross-validation statistical techniques along with other ML models. SVM) outperformed state-of-the-art accuracy 89.77%. efficiency all models was optimized Grid-based tuning, results obtained after showed significant improvement. XGB parameters 95.75%. implication learning approach clearly shows best compared This experimental analysis improved understanding above-defined methods enhanced their scope significance early detection disease.

Язык: Английский

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Explainable Artificial Intelligence of Multi-Level Stacking Ensemble for Detection of Alzheimer’s Disease Based on Particle Swarm Optimization and the Sub-Scores of Cognitive Biomarkers

IEEE Access, Год журнала: 2023, Номер 11, С. 123173 - 123193

Опубликована: Янв. 1, 2023

Alzheimer's disease (AD) is a progressive neurological disorder characterized by memory loss and cognitive decline, affecting millions worldwide. Early detection crucial for effective treatment, as it can slow progression improve quality of life. Machine learning has shown promise in AD using various medical modalities. In this paper, we propose novel multi-level stacking model that combines heterogeneous models modalities to predict different classes AD. The include sub-scores (e.g., clinical dementia rating – sum boxes, assessment scale) from the Disease Neuroimaging Initiative dataset. proposed approach, level 1, used six base (Random Forest (RF), Decision Tree (DT), Support Vector (SVM), Logistic Regression (LR), K-nearest Neighbors (KNN), Native Bayes (NB)to train each modality (ADAS, CDR, FQA). Then, build training outputs set staking testing outcomes set. 2, three are produced trains evaluates based on output 6 (RF, LR, DT, SVM, KNN, NB) combined Stacking meta-learners evaluate (RF). Finally, 3, prediction FQA) datasets merged new dataset, which testing. Training meta-learner, meta-learner produce final prediction. Our research also aims provide explanations, ensuring efficiency, effectiveness, trust through explainable artificial intelligence (XAI). Feature selection optimization Particle Swarm Optimization select most appropriate sub-scores. shows significant potential improving early diagnosis. results demonstrate multi-modality approach outperforms single-modality approaches. Moreover, achieve highest performance with selected features compared regular ML classifiers full multi-modalities, achieving accuracy, precision, recall, F1-scores 92.08%, 92.07%, 92.01% two classes, 90.03%, 90.19%, 90.05% respectively.

Язык: Английский

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SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2023, Номер 76(2), С. 2201 - 2216

Опубликована: Янв. 1, 2023

Breast cancer is a major public health concern that affects women worldwide. It leading cause of cancer-related deaths among women, and early detection crucial for successful treatment. Unfortunately, breast can often go undetected until it has reached advanced stages, making more difficult to treat. Therefore, there pressing need accurate efficient diagnostic tools detect at an stage. The proposed approach utilizes SqueezeNet with fire modules complex bypass extract informative features from mammography images. extracted are then utilized train support vector machine (SVM) image classification. SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, accuracy 94.10% sensitivity 94.30%. A 10-fold cross-validation was performed ensure the robustness mean standard deviation various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all indicators, indicating its superior performance. demonstrates effectiveness diagnosis using makes tool diagnosis. may have significant implications reducing mortality rates.

Язык: Английский

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Multi-engineered Graphene Extended-Gate Field-Effect Transistor for Peroxynitrite Sensing in Alzheimer’s Disease

ACS Nano, Год журнала: 2023, Номер 17(21), С. 21984 - 21992

Опубликована: Окт. 24, 2023

The expression of β-amyloid peptides (Aβ), a pathological indicator Alzheimer's disease (AD), was reported to be inapparent in the early stage AD. While peroxynitrite (ONOO–) is produced excessively and emerges earlier than Aβ plaques progression AD, it thus significant sensitively detect ONOO– for diagnosis AD its research. Herein, we unveiled an integrated sensor monitoring ONOO–, which consisted commercially available field-effect transistor (FET) high-performance multi-engineered graphene extended-gate (EG) electrode. In configuration presented EG electrode, laser-induced (LIG) intercalated with MnO2 nanoparticles (MnO2/LIG) can improve electrical properties LIG sensitivity sensor, oxide (GO)-MnO2/Hemin nanozyme isomerase activity selectively trigger isomerization NO3–. With this synergistic effect, our EG-FET respond high selectivity. Moreover, taking advantage modularly assembled portable sensing platform wireless tracking levels brain tissue transgenic mice at stages before massive appeared, systematically explored complex role occurrence development

Язык: Английский

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Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review

Frontiers in Neurology, Год журнала: 2024, Номер 15

Опубликована: Дек. 9, 2024

Neurodegenerative disorders (e.g., Alzheimer's, Parkinson's) lead to neuronal loss; neurocognitive delirium, dementia) show cognitive decline. Early detection is crucial for effective management. Machine learning aids in more precise disease identification, potentially transforming healthcare. This comprehensive systematic review discusses how machine (ML), can enhance early of these disorders, surpassing traditional diagnostics' constraints.

Язык: Английский

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