A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

International Journal of Telemedicine and Applications, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 24

Published: April 30, 2023

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered gather relevant scientific theoretical articles. Initially, 125 papers found between 2010 2021 related this integrated research field. After the filtering process, only 30 articles identified classified into five categories on their type methods. first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). second recurrent (RNN), 10% 3/30). third fourth categories, (DNN) long short-term memory (LSTM), account 6% 30). fifth restricted Boltzmann machine (RBM), 3% 1/30). literature's findings terms main aspects existing pattern recognition SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which six categories. Current challenges ensuring trustworthy BCI discussed, recommendations provided researchers developers. study critically reviews current unsolved issues development selection multicriteria decision-making (MCDM). trust proposal solution presented with three methodology phases evaluating benchmarking using fuzzy techniques. Valuable insights developers provided.

Language: Английский

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Latest clinical frontiers related to autism diagnostic strategies

Cell Reports Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 101916 - 101916

Published: Jan. 1, 2025

Language: Английский

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Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S1), P. 53 - 117

Published: June 21, 2023

Language: Английский

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Fuzzy Decision-Making Framework for Sensitively Prioritizing Autism Patients with Moderate Emergency Level

Applied Data Science and Analysis, Journal Year: 2023, Volume and Issue: unknown, P. 16 - 41

Published: March 15, 2023

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that requires careful assessment and management. The prioritization of ASD patients involves navigating through complexities such as conflicts, trade-offs, the importance different criteria. Therefore, this study focuses on prioritizing with in healthcare setting an evaluation benchmarking framework. aim to develop framework utilizes Multi-Criteria Decision Making (MCDM) methods assist professionals patients, particularly those moderate injury levels. methodology outlines several phases, including dataset identification, development decision matrix, weighting 19 criteria using FWZIC method, ranking 432 VIKOR evaluating proposed four sensitivity analysis scenarios. Among criteria, criterion 'verbal communication' obtained highest weight. Additionally, 'laughing for no reason', 'nodding', 'patient movement at home', 'pointing index finger' similar higher weights, indicating their potential impact patients. experimental results highlight significance adjusting weights influencing final rankings method. This emphasizes need consideration when assigning ensure accurate prioritization. Moreover, provides valuable insights into improving care support provided individuals autism Iraq. findings contribute existing body knowledge field pave way future research interventions aimed enhancing quality

Language: Английский

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Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review

Research in autism spectrum disorders, Journal Year: 2023, Volume and Issue: 108, P. 102228 - 102228

Published: Sept. 8, 2023

Language: Английский

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Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Journal of Personalized Medicine, Journal Year: 2023, Volume and Issue: 14(1), P. 41 - 41

Published: Dec. 28, 2023

(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to individual. Assistive technologies (ATs) play an important role in this context hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective study is analyze state integration AI ATs autism through review. (Methods) A review was conducted on PubMed Scopus, applying standard checklist qualification process. outcome reported 22 studies, including 7 reviews. (Key Content Findings) results reveal early yet promising interest integrating into assistive technologies. Exciting developments are currently underway at intersection robotics, as well creation wearable automated devices like smart glasses. These innovations offer substantial for enhancing communication, interaction, social engagement individuals autism. Presently, researchers prioritizing innovation over establishing solid presence within healthcare domain, where issues such regulation acceptance demand increased attention. (Conclusions) As field continues evolve, it becomes clear will pivotal bridging various domains, positioned act crucial connectors.

Language: Английский

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Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods

Informatics in Medicine Unlocked, Journal Year: 2022, Volume and Issue: 36, P. 101131 - 101131

Published: Nov. 16, 2022

Autism spectrum disorder (ASD) symptoms and severity levels vary from patient to patient, so treatment healthcare will vary. However, little attention has been given developing an autistic triage method for ASD patients concerning four issues: hybrid criteria, multi-selection criteria problems, importance, trade-off based on the inverse relationship between criteria. Therefore, this study aims develop a new triaging classifying them according their of using Fuzzy Multi-Criteria Decision Making (fMCDM) methods. Two methodology phases have conducted: first phase is identify preprocess dataset, including 988 with 42 medical Sociodemographic In second phase, two fMCDM methods were used method. The fuzzy Delphi Method (FDM) select most influential among thirteen psychologists in psychological field. Then Fuzzy-Weighted Zero-Inconsistency (FWZIC) assign weights important psychologists' opinions. Accordingly, Processes Triaging Patients (PTAP) developed time into three levels: minor, moderate, urgent. For preprocessed 538 out obtained as dataset underwent data cleaning capture only autism patients. FDM results selected 19 can control bias opinions, FWZIC assigned appropriate PTAP triages 36 minor injuries, 432 moderate 70 urgent injuries. More complex statistical analyses presented MedCalc software. Three physicians field gave subjective judgements diagnosis 46 random samples sensitivity 86.67%, 80%, 90.91%, while specificity 93.55%, 88.46%, 94.29% urgent, levels, respectively. addition, accuracy was 91.30% 84.78% 93.48% minor. This assessment led deduction that proposed be applied high performance. early application support clinical utilizing advantages techniques multidimensional Four psychologists, acquired 15 correlation analysis 'Wave' criterion highest level 0.4523. On contrary, "Pointing index finger" lowest −0.0542. Limitations future works also reported study. confirms efficacy compared previous studies five comparative points 100%.

Language: Английский

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An Umbrella Review of the Fusion of fMRI and AI in Autism

Diagnostics, Journal Year: 2023, Volume and Issue: 13(23), P. 3552 - 3552

Published: Nov. 28, 2023

The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central in autism diagnosis. integration Artificial Intelligence (AI) into the realm applications further contributes to its development. This study’s objective analyze emerging themes this domain through umbrella review, encompassing systematic reviews. research methodology was based on a structured process for conducting literature narrative using review PubMed and Scopus. Rigorous criteria, standard checklist, qualification were meticulously applied. findings include 20 reviews that underscore key research, particularly emphasizing significance technological integration, including pivotal roles fMRI AI. study also highlights enigmatic oxytocin. While acknowledging immense potential field, outcome does not evade significant challenges limitations. Intriguingly, there growing emphasis innovation AI, whereas aspects related healthcare processes, such as regulation, acceptance, informed consent, data security, receive comparatively less attention. Additionally, these Personalized Medicine (PM) represents promising yet relatively unexplored area within research. concludes by encouraging scholars focus critical health vital routine implementation applications.

Language: Английский

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Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(5), P. 6159 - 6188

Published: June 4, 2024

Abstract This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic solution is challenging resolving conflicts or trade-offs among ASD criteria. research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In first phase, identifies dataset, considering 19 critical medical and sociodemographic criteria The second phase introduces new Decision Matrix (DM) designed to manage effectively. third focuses extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) construct weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). fourth formulates Multi-Attributive Border Approximation Area Comparison (MABAC) method rank within each level. Results from SVN2TL-FWZIC offer significant insights, including higher values "C12 = Laughing no reason" "C16 Notice sound bell" with 0.097358 0.083832, indicating their significance in identifying potential symptoms. base prioritizing triage levels MABAC, encompassing behavioral dimensions. methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming consistency results points. compares benchmark studies, distinct points, achieves remarkable 100% congruence these prior investigations. implications this are far-reaching, offering valuable guide clinical psychologists cases patients.

Language: Английский

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Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Computational and Mathematical Methods in Medicine, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 19

Published: Nov. 16, 2022

Background and Contexts. Autism spectrum disorder (ASD) is difficult to diagnose, prompting researchers increase their efforts find the best diagnosis by introducing machine learning (ML). Recently, several available challenges issues have been highlighted for of ASD. High consideration must be taken into feature selection (FS) approaches classification process simultaneously using medical tests sociodemographic characteristic features in autism diagnostic. The constructed ML models neglected importance a training evaluation dataset, especially since some different contributions processing data possess more relevancies information than others. However, role physician’s experience towards remains limited. In addition, presence many criteria, criteria trade-offs, categorize benchmarking concerning intersection between FS methods given under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an framework hybrid classify patients’ emergency levels considering solutions. Method. three-phase integrated MCDM develop evaluate benchmark best. Firstly, new ASD-dataset-combined identified preprocessed. Secondly, developing three techniques five algorithms introduces 15 models. selected from each technique are weighted before feeding fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate dynamic decision matrix all developed seven metrics, including accuracy, precision, F1 score, recall, test time, train AUC. (ii) fuzzy opinion score (FDOSM) used metrics. Results. Results reveal that obtained size others number features; sets were 39, 38, 41 out 48 features. Each set its weights FWIZC. Considered mostly within techniques. first “ReF-decision tree,” “IG-decision “Chi2-decision with values 0.15714, 0.17539, 0.29444. model (ReF-decision tree) 0.4190, 0.0030, 0.9946, 0.9902, 0.9951 C1=train C2=test C3=AUC, C4=CA, C5=F1 C6=precision, C7=recall, respectively. would beneficial advancing, accelerating, selecting tools therapy can identify severity as light, medium, or intense

Language: Английский

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