Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Frontiers in Molecular Neuroscience, Journal Year: 2022, Volume and Issue: 15

Published: Oct. 4, 2022

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD also associated with communication deficits repetitive behavior affected individuals. Various detection methods have been developed, including neuroimaging modalities psychological tests. Among these methods, magnetic resonance imaging (MRI) are of paramount importance to physicians. Clinicians rely on MRI diagnose accurately. The non-invasive include functional (fMRI) structural (sMRI) methods. However, diagnosing fMRI sMRI for specialists often laborious time-consuming; therefore, several computer-aided design systems (CADS) based artificial intelligence (AI) developed assist specialist Conventional machine learning (ML) deep (DL) the most popular schemes AI used ASD. This study aims review automated using AI. We CADS ML techniques diagnosis modalities. There has very limited work use DL develop diagnostic models A summary studies provided Supplementary Appendix. Then, challenges encountered during described detail. Additionally, graphical comparison automatically discussed. suggest future approaches detecting ASDs neuroimaging.

Language: Английский

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Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 160, P. 106998 - 106998

Published: May 6, 2023

Language: Английский

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A novel memristive neuron model and its energy characteristics

Cognitive Neurodynamics, Journal Year: 2024, Volume and Issue: 18(4), P. 1989 - 2001

Published: Jan. 28, 2024

Language: Английский

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Early attention-deficit/hyperactivity disorder (ADHD) with NeuroDCT-ICA and rhinofish optimization (RFO) algorithm based optimized ADHD-AttentionNet

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 26, 2025

The ADHD detector analyzes behavioral, cognitive, or physiological data (e.g., EEG, eye-tracking, surveys) to identify patterns associated with symptoms. This work offers a more sophisticated method of detecting by overcoming the main drawbacks existing approaches in terms processing, detection accuracy, and computational time. is inspired fact that Deep Learning (DL) frameworks could transform systems ADHD. In proposed framework, there new NeuroDCT-ICA module for preprocessing raw EEG data, which guarantees elimination noise extraction informative features. Moreover, introduces novel RhinoFish Optimization (RFO) algorithm selecting optimal features, enhance processing capacity stability system. As core approach, ADHD-AttentionNet - deep learning-based model aimed at improving accuracy confidence identification. validated standard metrics, performance outstanding as it has high 98.52%, F-score 98.26% specificity 98.16%. These outcomes show yields better related patterns.

Language: Английский

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Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107484 - 107484

Published: Jan. 6, 2025

Language: Английский

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Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

Information Fusion, Journal Year: 2022, Volume and Issue: 93, P. 85 - 117

Published: Dec. 14, 2022

Language: Английский

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Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Biomedicines, Journal Year: 2023, Volume and Issue: 11(7), P. 1858 - 1858

Published: June 29, 2023

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. current gold standard ASD diagnosis based on behavior observational tests clinicians, which being subjective time-consuming afford only late detection (a child must have mental age at least two to apply an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability assist fast, objective, early detection. With the recent advances artificial intelligence (AI) machine learning (ML) techniques, sufficient tools been developed both automated More recently, development deep (DL), young subfield AI neural networks (ANNs), has successfully enabled processing MRI data improved diagnostic abilities. This survey focuses role autism diagnostics basic modalities: diffusion tensor (DTI) functional (fMRI). In addition, outlines findings DTI fMRI autism. Furthermore, techniques using are summarized discussed. Finally, emerging tendencies described. results this study show how useful early, diagnosis. solutions potential be used healthcare settings will introduced future.

Language: Английский

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Staphylococcus Aureus-Related antibiotic resistance detection using synergy of Surface-Enhanced Raman spectroscopy and deep learning

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 91, P. 105933 - 105933

Published: Jan. 10, 2024

Language: Английский

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Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

Applied Intelligence, Journal Year: 2023, Volume and Issue: 54(1), P. 35 - 79

Published: Dec. 5, 2023

Language: Английский

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Diagnosis of Parkinson Disease from EEG Signals Using a CNN-LSTM Model and Explainable AI

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 128 - 138

Published: Jan. 1, 2024

Language: Английский

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