Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Construction of a Structurally Unbiased Brain Template with High Image Quality from MRI Scans of Saudi Adult Females.

Bioengineering (Basel, Switzerland)·2025

Same author

Serum Ghrelin and Leptin Concentrations in Patients with Major Depressive Disorder before and after Supplementation with Vitamin D3.

Depression and anxiety·2025

Same author

Personality and Attachment Patterns in Patients with Psychogenic Non-Epileptic Seizures in Saudi Arabia.

Medicina (Kaunas, Lithuania)·2025

Same author

Functional neurological disorder in Saudi Arabia: A retrospective study.

International journal of psychiatry in medicine·2023

Same author

Alzheimer's Behavioral and Psychiatric Burden in Saudi Arabia: Validation of the Saudi Version of the Neuropsychiatric Inventory.

Alzheimer disease and associated disorders·2023

Same author

The Role of Religious Culture in Medical Professionalism in a Muslim Arab Society.

Perspectives on medical education·2023

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Esraa H Alyoubi¹, Kawthar M Moria¹, Jamaan S Alghamdi²

¹Department of Computer Science, College of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This summary is machine-generated.

This study optimized deep learning models to predict mild cognitive impairment (MCI) using only the entorhinal cortex from MRI scans. The Inception-V3 model achieved 70% accuracy, showing promise for earlier MCI diagnosis.

Keywords:

deep learning entorhinal cortex magnetic resonance imaging mild cognitive impairments transfer learning

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Related Experiment Videos

Last Updated: Jul 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Early diagnosis of mild cognitive impairment (MCI) using magnetic resonance imaging (MRI) improves patient outcomes.
Deep learning models are increasingly used for cost-effective MCI prediction.
The entorhinal cortex shows early atrophy in MCI, making it a key diagnostic area, though its small size limits research.

Purpose of the Study:

To develop and optimize deep learning models for differentiating MCI from normal controls using entorhinal cortex MRI data.
To investigate the efficacy of specific neural network architectures for MCI prediction based on entorhinal cortex features.

Main Methods:

A dataset focused exclusively on the entorhinal cortex region was constructed for MCI classification.
Three neural network architectures (VGG16, Inception-V3, ResNet50) were independently optimized for feature extraction.
A convolution neural network classifier was employed with the optimized architectures.

Main Results:

The Inception-V3 architecture achieved the best performance for feature extraction.
Key performance metrics included 70% accuracy, 90% sensitivity, 54% specificity, and 69% area under the curve.
The model demonstrated an F1 score of 73%, indicating a balanced precision-recall performance.

Conclusions:

The proposed deep learning approach using entorhinal cortex MRI data is effective for predicting MCI.
This method shows potential for improving the early diagnosis of MCI, complementing existing diagnostic tools.