Search research 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

Related Concept Videos

Visual Agnosia

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...

Prosopagnosia

Prosopagnosia

Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...

You might also read

Related Articles

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

Sort by

Same author

Efficient synthesis of syringyl alcohol through bioreduction of lignin-derived syringaldehyde by newly constructed recombinant Escherichia coli C165F-V231D.

Bioresource technology·2026

Same author

The CBS/H2S axis regulates intestinal stem cell homeostasis and radiation-induced intestinal damage.

Stem cell research & therapy·2025

Same author

The Role of the Microenvironment on the Localization of Hair and Skin Nonuniform Pigmented Spots in Mice with a Targeted Mutation in the Endothelin B Receptor.

The Journal of investigative dermatology·2025

Same author

Mechanical confinement governs phenotypic plasticity in melanoma.

Nature·2025

Same author

Spatial deformation prediction method of fractured rock tunnel based on quantified GSI and its application.

Scientific reports·2024

Same author

Next-generation sequencing identified that RET variation associates with lymph node metastasis and the immune microenvironment in thyroid papillary carcinoma.

BMC endocrine disorders·2024

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

Search research articles

Related Experiment Video

Updated: Aug 10, 2025

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

Published on: May 23, 2013

Vertical Nystagmus Recognition Based on Deep Learning.

Haibo Li¹, Zhifan Yang¹

¹College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Shanghai 201620, China.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces an AI-based method for recognizing vertical nystagmus, improving diagnostic accuracy for dizziness. The deep learning approach achieved 91% average accuracy, outperforming traditional methods.

Keywords:

convolutional attention deep learning depthwise separable convolution vertical nystagmus

More Related Videos

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Related Experiment Videos

Last Updated: Aug 10, 2025

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

Published on: May 23, 2013

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Area of Science:

Neuro-ophthalmology
Vestibular Medicine
Artificial Intelligence in Healthcare

Background:

Vertical nystagmus is a key neuro-ophthalmic sign in vestibular disorders, indicating semicircular canal and otolith function.
Accurate diagnosis of dizziness relies on identifying nystagmus, but traditional visual observation can be subjective and experience-dependent.
Advancements in AI offer potential for objective and accurate nystagmus detection.

Purpose of the Study:

To develop and evaluate a deep learning-based method for automated vertical nystagmus recognition.
To improve the accuracy and objectivity of vertical nystagmus diagnosis in clinical settings.
To provide a reliable AI tool for medical experts in diagnosing dizziness.

Main Methods:

A novel deep learning model incorporating dilated convolution, depthwise separable convolution, convolution attention, and BiLSTM-GRU modules was designed.
The model was trained and tested on a dataset for vertical nystagmus recognition.
Performance was evaluated based on average recognition accuracy.

Main Results:

The proposed deep learning method achieved an average recognition accuracy of 91% for vertical nystagmus.
This AI-driven approach demonstrated a 2% higher recognition accuracy compared to other existing methods on the same dataset.
The results indicate the effectiveness of the proposed model in accurately identifying vertical nystagmus.

Conclusions:

Deep learning offers a promising, accurate, and objective approach for vertical nystagmus recognition.
The developed AI method can assist medical experts in diagnosing dizziness, potentially reducing subjective bias.
This technology has the potential to enhance diagnostic efficiency and accuracy in vestibular medicine.