Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data, Volume II

35.7K

views

100

authors

11

articles

Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data, Volume II

35.7K

views

100

authors

11

articles

Editorial

12 July 2023

Editorial: Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data, volume II

Fahmi Khalifa

,

Ahmed Shalaby

,

Ahmed Soliman

,

Safa Elaskary

, 1 more and

Mohamed Abdelazim

1,947 views

0 citations

Editors

6

University of Texas Southwestern Medical Center

Morgan State University

University of Louisville

Ahmed Refaey Hussein

Manhattan College

Mohamed Abdelazim

Department of Electrical Engineering, Faculty of Engineering, Mansoura University

King Abdulaziz University

Impact

Original Research

14 June 2023

Abnormal flow pattern of low wall shear stress and high oscillatory shear index in spontaneous vertebral artery dissection with vertebral artery hypoplasia

Jiajia Bao

, 6 more and

Li He

2,246 views

3 citations

Original Research

11 April 2023

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

Yuanlin Wang

, 8 more and

Yuting Zhang

2,271 views

6 citations

Original Research

27 March 2023

Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume

Wenjing Zhu

, 3 more and

Zhijun Zhang

2,585 views

3 citations

Original Research

24 March 2023

EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task

Yunzhe Li

, 5 more and

Shu Zhou

3,499 views

8 citations

Original Research

23 March 2023

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

Antonio Ricciardi

, 10 more and

Claudia A. M. Gandini Wheeler-Kingshott

2,189 views

0 citations

Original Research

13 March 2023

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

Junwei Duan

, 4 more and

C. L. Philip Chen

7,502 views

16 citations

Original Research

07 March 2023

Disrupted interhemispheric coordination of sensory-motor networks and insula in major depressive disorder

Chunguo Zhang

, 11 more and

Wenbin Guo

Objective: Prior researches have identified distinct differences in neuroimaging characteristics between healthy controls (HCs) and patients with major depressive disorder (MDD). However, the correlations between homotopic connectivity and clinical characteristics in patients with MDD have yet to be fully understood. The present study aimed to investigate common and unique patterns of homotopic connectivity and their relationships with clinical characteristics in patients with MDD.

Methods: We recruited 42 patients diagnosed with MDD and 42 HCs. We collected a range of clinical variables, as well as exploratory eye movement (EEM), event-related potentials (ERPs) and resting-state functional magnetic resonance imaging (rs-fMRI) data. The data were analyzed using correlation analysis, support vector machine (SVM), and voxel-mirrored homotopic connectivity (VMHC).

Results: Compared with HCs, patients with MDD showed decreased VMHC in the insula, and increased VMHC in the cerebellum 8/vermis 8/vermis 9 and superior/middle occipital gyrus. SVM analysis using VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula, or VMHC values in the superior/middle occipital gyrus and insula as inputs can distinguish HCs and patients with MDD with high accuracy, sensitivity, and specificity.

Conclusion: The study demonstrated that decreased VMHC in the insula and increased VMHC values in the sensory-motor networks may be a distinctive neurobiological feature for patients with MDD, which could potentially serve as imaging markers to discriminate HCs and patients with MDD.

3,271 views

18 citations

Original Research

09 January 2023

A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images

Rizwan Khan

, 6 more and

Zhonglong Zheng

5,159 views

45 citations

Original Research

22 February 2023

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke

Qingqing Xu

, 8 more and

Wanqun Yang

2,827 views

5 citations

Original Research

19 July 2022

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

Xin Tan

, 13 more and

Shijun Qiu

2,361 views

5 citations

Open for submission

Frontiers in Neuroscience

Unraveling Cognitive Impairment: A Multimodal MRI Approach to Brain Networks

Edited by Shiyan Yan, Jinhuan Yue, Xiao-ling Li

Deadline

30 July 2025

Recommended Research Topics

Frontiers in Neuroscience

Artificial Intelligence based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data

Edited by Ahmed Shalaby, Safa Elaskary, Fahmi Khalifa, Ahmed Refaey Hussein, Ahmed Soliman, Mohamed Abdelazim

70.8K

views

114

authors

14

articles

Frontiers in Neuroscience

Physiological Noise in fMRI Data and Brain Resting-state Functional Connectivity

Edited by Noorbakhsh Amiri Golilarz, Hasan Demirel

32K

views

37

authors

6

articles

Frontiers in Neuroscience

Deep Learning techniques and their applications to the healthy and disordered brain - during development through adulthood and beyond

Edited by Amir Shmuel, Albert Yang, Yogesh Rathi, Hyunjin Park

95.2K

views

91

authors

12

articles

Frontiers in Neuroscience

New Challenges and Future Perspectives of Artificial Intelligence in Neuroimaging Data Analysis

Edited by Long Lu, Pengmin Qin, Hongsheng Liu, Ye Chen

20.2K

views

51

authors

6

articles

Frontiers in Neuroscience

Exploring the intersection of neuroimaging and artificial intelligence: What is the interplay?

Edited by Indranath Chatterjee, Wael MY Mohamed, Sahil Bajaj

12.1K

views

30

authors

4

articles

About

This Research Topic is part of the Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data series:

Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data, Volume I

There has been an exponential growth recently in research projects studying the etiology and mechanisms of several brain disorders, such as autism, Multiple sclerosis (MS), Alzheimer’s Disease (AD), Epilepsy and Parkinson’s. In recent years, the utility of artificial intelligence (AI) has been explored in a variety of research arenas for modern computer-aided diagnosis (CAD). The use of medical imaging and the characteristic examples provided by medical experts in AI-based CADs is a growing field for more accurate extraction of reliable diagnostic cues, which can eventually help physicians for more appropriate and personalized treatments. For instance, texture analyses of the white matter on brain T2-weighted magnetic resonance imaging (MRI) can help in diagnosing multiple sclerosis (MS).

Traditional machine learning-based CAD systems employ many learning techniques that are often tailored to a specific application and usually fail if tested outside the training data sets. Advances in AI techniques, particularly, end-to-end deep learning, combined with recent progress in neuroimaging technologies ( e.g., diffusion-weighted MRI and other modalities for visualizing the brain and the nervous system) have created exciting new opportunities for both enhancing traditional machine learning methods and applying prospective new ones to predict or provide better diagnosis of a patient’s brain.

The focus of this research topic is on the recent AI-based CAD systems for the analysis of medical imaging data from patients with brain disorders such as MS, Autism, Alzheimer's, etc. Researchers around the globe are invited to contribute their original research articles and/or reviews stimulating technological advances in current applications in an effort to provide solutions to patients suffering from those devastating disorders.

We especially welcome submissions involving:

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of multiple sclerosis from MRI data (e.g., T2- weighted)

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of autism spectrum disorder from MR brain images (e.g., structural, functional, and diffusion tensor MRI).

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of Alzheimer’s disease from MRI data (such as structural, functional, and diffusion tensor MR images).

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of other brain disorders, such as epilepsy and Parkinson’s disease using structural and functional MRI as well as SPECT.

This Research Topic is part of the Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data series:

Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data, Volume I

There has been an exponential growth recently in research projects studying the etiology and mechanisms of several brain disorders, such as autism, Multiple sclerosis (MS), Alzheimer’s Disease (AD), Epilepsy and Parkinson’s. In recent years, the utility of artificial intelligence (AI) has been explored in a variety of research arenas for modern computer-aided diagnosis (CAD). The use of medical imaging and the characteristic examples provided by medical experts in AI-based CADs is a growing field for more accurate extraction of reliable diagnostic cues, which can eventually help physicians for more appropriate and personalized treatments. For instance, texture analyses of the white matter on brain T2-weighted magnetic resonance imaging (MRI) can help in diagnosing multiple sclerosis (MS).

Traditional machine learning-based CAD systems employ many learning techniques that are often tailored to a specific application and usually fail if tested outside the training data sets. Advances in AI techniques, particularly, end-to-end deep learning, combined with recent progress in neuroimaging technologies ( e.g., diffusion-weighted MRI and other modalities for visualizing the brain and the nervous system) have created exciting new opportunities for both enhancing traditional machine learning methods and applying prospective new ones to predict or provide better diagnosis of a patient’s brain.

The focus of this research topic is on the recent AI-based CAD systems for the analysis of medical imaging data from patients with brain disorders such as MS, Autism, Alzheimer's, etc. Researchers around the globe are invited to contribute their original research articles and/or reviews stimulating technological advances in current applications in an effort to provide solutions to patients suffering from those devastating disorders.

We especially welcome submissions involving:

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of multiple sclerosis from MRI data (e.g., T2- weighted)

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of autism spectrum disorder from MR brain images (e.g., structural, functional, and diffusion tensor MRI).

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of Alzheimer’s disease from MRI data (such as structural, functional, and diffusion tensor MR images).

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of other brain disorders, such as epilepsy and Parkinson’s disease using structural and functional MRI as well as SPECT.

Download ebook

Share

Editors

University of Texas Southwestern Medical Center

Morgan State University

University of Louisville

Ahmed Refaey Hussein

Manhattan College

Mohamed Abdelazim

Department of Electrical Engineering, Faculty of Engineering, Mansoura University

King Abdulaziz University

Impact

35,675 Total views

24,724 Article views

10,212 Article downloads

739 Topic views

Published In

Journal Thumbnail

Frontiers in Neuroscience

Brain Imaging Methods

Journal Thumbnail

Frontiers in Neuroimaging

Brain Imaging Methods

Journal Thumbnail

Frontiers in Neurology

Brain Imaging Methods

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Suggest a topic