Jove
Visualize
Contact Us

Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

628
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
628
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

247
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
247
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

501
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
501
Aggregates Classification01:29

Aggregates Classification

366
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
366
Associative Learning01:27

Associative Learning

522
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
522

You might also read

Related Articles

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

Sort by
Same author

EAMNet: an Alzheimer's disease prediction model based on representation learning.

Physics in medicine and biology·2023
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

1.2K

Alzheimer's Disease Prediction Algorithm Based on Group Convolution and a Joint Loss Function.

Jiayuan Cheng1, Huabin Wang1, Shicheng Wei2

  • 1International Brain Science and Engineering Center, School of Computer Science and Technology, Anhui University, Hefei 230039, China.

Computational and Mathematical Methods in Medicine
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for predicting Alzheimer's disease (AD) using brain imaging. The enhanced method improves prediction accuracy for mild cognitive impairment (MCI) patients by refining lesion feature extraction.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

7.6K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

8.0K

Related Experiment Videos

Last Updated: Aug 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

1.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

7.6K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

8.0K

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is effective for Alzheimer's disease (AD) prediction.
  • Current PET imaging faces challenges like indistinct features, low signal-to-noise ratios, and artefacts, impacting accuracy, especially for mild cognitive impairment (MCI).

Purpose of the Study:

  • To develop an improved AD prediction algorithm using advanced deep learning techniques.
  • To enhance the accuracy and reliability of AD prediction from PET images, particularly for MCI patients.

Main Methods:

  • A group convolutional backbone network (ResNet18-based) was designed for multi-channel lesion feature extraction.
  • A hybrid attention mechanism was incorporated to focus on relevant diagnostic regions and learn feature weights.
  • A joint loss function, combining cross-entropy with regularization, was proposed to prevent overfitting and improve generalization.

Main Results:

  • The proposed algorithm demonstrated improved lesion feature extraction and enhanced learning of disease-relevant regions.
  • Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed a 2.4% increase in prediction accuracy compared to existing methods.
  • The algorithm effectively addressed issues of indistinct features and artefacts in PET images.

Conclusions:

  • The developed algorithm significantly improves AD prediction accuracy, especially for MCI.
  • The combination of group convolution, attention mechanisms, and a joint loss function offers a more robust approach to neuroimaging-based disease prediction.
  • This method shows promise for earlier and more accurate diagnosis of Alzheimer's disease.