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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

399
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
399
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.9K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.9K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

10.8K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
10.8K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

9.9K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
9.9K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.7K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.7K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.1K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.1K

You might also read

Related Articles

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

Sort by
Same author

Sparse-Observation Multi-Horizon Glaucoma Progression Forecasting with Biologically Constrained Temporal Consistency: A Glaucoma Case Study.

Research square·2026
Same author

Detecting glaucoma progression through optic nerve head hemoglobin concentration using automated colorimetric analysis.

European journal of ophthalmology·2026
Same author

Convolutional Graph Isomorphism Network to Detect Glaucomatous Visual Field Defects.

Ophthalmology science·2026
Same author

The association of cataract surgery with risk of falls and fractures among Medicare enrollees with cataract.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Influence of Intraocular Pressure on Clinical Decision-Making in Glaucoma Management.

JAMA ophthalmology·2026
Same author

Performance of a Small Language Model Versus a Large Language Model in Answering Glaucoma Frequently Asked Patient Questions: Development and Usability Study.

JMIR AI·2026

Related Experiment Video

Updated: Jan 22, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.9K

Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep

Alessandro A Jammal1,2, Atalie C Thompson1, Nara G Ogata1

  • 1Vision, Imaging and Performance Laboratory (VIP), Duke Eye Center and Department of Ophthalmology, Duke University, Durham, NC, USA.

Scientific Reports
|July 10, 2019
PubMed
Summary

A new deep learning (DL) algorithm accurately detects segmentation errors in retinal nerve fibre layer (RNFL) images from spectral-domain optical coherence tomography (SDOCT) scans, improving diagnostic reliability.

More Related Videos

In Vivo Imaging of Cx3cr1gfp/gfp Reporter Mice with Spectral-domain Optical Coherence Tomography and Scanning Laser Ophthalmoscopy
06:19

In Vivo Imaging of Cx3cr1gfp/gfp Reporter Mice with Spectral-domain Optical Coherence Tomography and Scanning Laser Ophthalmoscopy

Published on: November 11, 2017

11.2K
Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo
08:17

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

Published on: September 22, 2017

20.0K

Related Experiment Videos

Last Updated: Jan 22, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.9K
In Vivo Imaging of Cx3cr1gfp/gfp Reporter Mice with Spectral-domain Optical Coherence Tomography and Scanning Laser Ophthalmoscopy
06:19

In Vivo Imaging of Cx3cr1gfp/gfp Reporter Mice with Spectral-domain Optical Coherence Tomography and Scanning Laser Ophthalmoscopy

Published on: November 11, 2017

11.2K
Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo
08:17

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

Published on: September 22, 2017

20.0K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate segmentation of the retinal nerve fibre layer (RNFL) is crucial for diagnosing eye diseases using spectral-domain optical coherence tomography (SDOCT).
  • Manual review of SDOCT B-scans for segmentation errors is time-consuming and prone to human error.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) algorithm for automated detection of RNFL segmentation errors in SDOCT B-scans.
  • To establish the performance of the DL algorithm against human grading as the reference standard.

Main Methods:

  • A dataset of 25,250 SDOCT B-scans, previously reviewed for segmentation errors by human graders, was utilized.
  • The dataset was randomly split into training/validation (50%) and testing (50%) sets.
  • A DL algorithm was trained to identify segmentation errors, and its performance was assessed using the area under the receiver operating characteristic (ROC) curve and overall accuracy.

Main Results:

  • The DL algorithm demonstrated high performance, achieving an area under the ROC curve of 0.979 and an overall accuracy of 92.4%.
  • The algorithm showed significantly different mean DL probabilities of segmentation error between scans with (0.90 ± 0.17) and without (0.12 ± 0.22) errors (P < 0.001).
  • The DL algorithm achieved 98.9% sensitivity in detecting severe segmentation errors.

Conclusions:

  • The developed DL algorithm effectively and accurately detects RNFL segmentation errors in SDOCT B-scans.
  • This automated tool can assist clinicians and researchers in efficiently reviewing SDOCT images, reducing the risk of diagnostic inaccuracies caused by artifacts.