Jove
Visualize
Contact Us
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 Concept Videos

Pulse amplitude and quality01:17

Pulse amplitude and quality

3.2K
Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
3.2K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Leaky Scanning02:28

Leaky Scanning

5.7K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.7K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Force Classification01:22

Force Classification

2.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.4K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Pachychoroid disease: review and update.

Eye (London, England)·2024
Same author

Challenges in posterior uveitis-tips and tricks for the retina specialist.

Journal of ophthalmic inflammation and infection·2023
Same author

Baseline demographic, clinical and multimodal imaging features of young patients with type 2 macular telangiectasia.

International journal of retina and vitreous·2023
Same author

Evaluation of baseline optic disc pit and optic disc coloboma maculopathy features by spectral domain optical coherence tomography.

International journal of retina and vitreous·2023
Same author

Prevalence of choroidal nevi in patients with central serous chorioretinopathy.

Therapeutic advances in ophthalmology·2023
Same author

Determinants for Anemic Retinopathy.

Beyoglu eye journal·2023

Related Experiment Video

Updated: Feb 6, 2026

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

1.0K

Amplitude-scan classification using artificial neural networks.

Kunal K Dansingani1, Kiran Kumar Vupparaboina2,3, Surya Teja Devarkonda3

  • 1Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA. kkd@doctor.com.

Scientific Reports
|August 22, 2018
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANN) can classify optical coherence tomography (OCT) scan data. This machine learning approach accurately distinguishes disease signatures from OCT amplitude-scan reflectivity profiles, even with limited training data.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K

Related Experiment Videos

Last Updated: Feb 6, 2026

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

1.0K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K

Area of Science:

  • Biomedical Engineering
  • Ophthalmology
  • Machine Learning

Background:

  • Optical coherence tomography (OCT) noninvasively images semi-transparent tissues using backscatter and interferometry.
  • Automated image classification using machine learning is of significant interest in ophthalmology for OCT diagnostics.
  • Standard OCT images (2D/3D) are human-readable, but the fundamental amplitude-scan reflectivity profile is difficult for humans to interpret.

Purpose of the Study:

  • To determine if a feed-forward artificial neural network (ANN) can distinguish and classify disease signatures from OCT amplitude-scan reflectivity profiles.
  • To assess the accuracy and generalization capabilities of the ANN classifier on OCT data.

Main Methods:

  • Utilized a feed-forward artificial neural network (ANN) as a classifier.
  • Trained the ANN on amplitude-scan reflectivity profiles from OCT scans.
  • Evaluated classifier performance on a dataset including 24 eyes.

Main Results:

  • The ANN classifier achieved high accuracy in distinguishing disease signatures.
  • The classifier demonstrated good generalization capabilities on unseen OCT data.
  • Successful classification was achieved with a relatively small training dataset.

Conclusions:

  • Feed-forward ANNs can effectively classify disease signatures from OCT amplitude-scan reflectivity profiles.
  • This machine learning approach offers a promising method for automated OCT diagnostics.
  • The classifier's capabilities can be expanded to include rare diseases and other scientific disciplines.