Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review DOI Creative Commons
Gopi Battineni, Nalini Chintalapudi, Mohammad Amran Hossain

et al.

Bioengineering, Journal Year: 2022, Volume and Issue: 9(8), P. 370 - 370

Published: Aug. 5, 2022

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by increased incidence several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, treatment preventive measures, an early, accurate diagnosis necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for neurological Increasing evidence indicates that association artificial intelligence (AI) approaches with MRI particularly useful improving diagnostic accuracy different types. Objectives: In this work, we have systematically reviewed characteristics AI algorithms early detection disorders, also discussed its performance metrics. Methods: A document search was conducted three databases, namely PubMed (Medline), Web Science, Scopus. limited to articles published after 2006 English only. screening performed using quality criteria based on Newcastle–Ottawa Scale (NOS) rating. Only papers NOS score ≥ 7 were considered further review. Results: produced count 1876 and, because duplication, 1195 not considered. Multiple screenings assess criteria, which yielded 29 studies. All selected grouped attributes, study type, type model used identification dementia, metrics, data type. Conclusions: most disorders occurring Alzheimer’s disease vascular dementia. techniques associated resulted ranging from 73.3% 99%. These findings suggest should be conventional obtain precise old age.

Language: Английский

ADNet: deep learning based model for Alzheimer’s disease classification DOI
Prashant Upadhyay, Pradeep Tomar, Satya Prakash Yadav

et al.

Iran Journal of Computer Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

Citations

1
A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer DOI Creative Commons
Nadiah A. Baghdadi, Amer Malki, Hossam Magdy Balaha

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(11), P. 4250 - 4250

Published: June 2, 2022

Alzheimer's disease (AD) is a chronic that affects the elderly. There are many different types of dementia, but one leading causes death. AD brain disorder leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, or personality changes, and ultimately death due dementia. Unfortunately, no cure has yet been developed for it, it known causes. Clinically, imaging tools can aid in diagnosis, deep learning recently emerged as an important component these tools. Deep requires little image preprocessing infer optimal data representation from raw images without prior feature selection. As result, they produce more objective less biased process. The performance convolutional neural network (CNN) primarily affected by hyperparameters chosen dataset used. A model classifying patients using transfer optimized Gorilla Troops early diagnosis. This study proposes A3C-TL-GTO framework MRI classification detection. empirical quantitative accurate automatic classification, evaluated Dataset (four classes images) Disease Neuroimaging Initiative (ADNI). proposed reduces bias variability steps optimization classifier Our strategy, on MRIs, easily adaptable other methods. According our findings, was excellent instrument this task, significant potential advantage patient care. ADNI dataset, online disease, used obtain magnetic resonance (MR) images. experimental results demonstrate achieves 96.65% accuracy 96.25% dataset. Moreover, better terms demonstrated over state-of-the-art approaches.

Language: Английский

Citations

37
A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR images DOI
Neha Garg, Mahipal Singh Choudhry, Rajesh M. Bodade

et al.

Journal of Neuroscience Methods, Journal Year: 2022, Volume and Issue: 384, P. 109745 - 109745

Published: Nov. 14, 2022

Language: Английский

Citations

36
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

et al.

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 82(8), P. 12699 - 12717

Published: Sept. 26, 2022

Language: Английский

Citations

35
Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review DOI Creative Commons
Gopi Battineni, Nalini Chintalapudi, Mohammad Amran Hossain

et al.

Bioengineering, Journal Year: 2022, Volume and Issue: 9(8), P. 370 - 370

Published: Aug. 5, 2022

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by increased incidence several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, treatment preventive measures, an early, accurate diagnosis necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for neurological Increasing evidence indicates that association artificial intelligence (AI) approaches with MRI particularly useful improving diagnostic accuracy different types. Objectives: In this work, we have systematically reviewed characteristics AI algorithms early detection disorders, also discussed its performance metrics. Methods: A document search was conducted three databases, namely PubMed (Medline), Web Science, Scopus. limited to articles published after 2006 English only. screening performed using quality criteria based on Newcastle–Ottawa Scale (NOS) rating. Only papers NOS score ≥ 7 were considered further review. Results: produced count 1876 and, because duplication, 1195 not considered. Multiple screenings assess criteria, which yielded 29 studies. All selected grouped attributes, study type, type model used identification dementia, metrics, data type. Conclusions: most disorders occurring Alzheimer’s disease vascular dementia. techniques associated resulted ranging from 73.3% 99%. These findings suggest should be conventional obtain precise old age.

Language: Английский

Citations

31