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

You might also read

Related Articles

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

Sort by
Same author

Multi-dimensional predictive model for diminished ovarian reserve in Hashimoto's thyroiditis: development and application.

Reproductive biology and endocrinology : RB&E·2026
Same author

Dielectric Modulation of Ionization Energetics in Organotin Extreme Ultraviolet Photoresists.

ACS applied materials & interfaces·2026
Same author

Pyramiding <i>nn1</i>6 and <i>rin1</i> alleles to balance plant height and node number at high latitudes.

Molecular breeding : new strategies in plant improvement·2026
Same author

An esophageal-pleural fistula following transesophageal echocardiography-guided left atrial appendage closure: a case report.

Frontiers in medicine·2026
Same author

Liquid-phase microextraction assisted surface-enhanced Raman spectroscopy: On-site quantification of amphetamine-type stimulants in complex samples from crime scenes.

Analytica chimica acta·2026
Same author

Corrigendum: Ybx1 deficiency impairs spermatid development and male fertility without affecting meiosis in mice: insights into spermatogenesis.

The Journal of reproduction and development·2026

Related Experiment Video

Updated: Sep 11, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.5K

Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning

Hongxia Hao1, Jiemin Chen1, Yifei Yan2,3

  • 1Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China.

Frontiers in Cell and Developmental Biology
|August 18, 2025
PubMed
Summary

Machine learning models analyzing pattern visual evoked potentials (PVEPs) accurately predict best corrected visual acuity (BCVA) in patients with ocular trauma. These PVEP-based models offer a promising tool for clinical evaluation post-injury.

Keywords:
best corrected visual acuitymachine learningpattern visual evoked potentialsvisual acuityvisual evoked potential

More Related Videos

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

2.8K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.9K

Related Experiment Videos

Last Updated: Sep 11, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.5K
Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

2.8K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.9K

Area of Science:

  • Ophthalmology
  • Machine Learning
  • Biomedical Engineering

Background:

  • Ocular trauma can significantly impact visual acuity.
  • Predicting stabilized visual acuity is crucial for patient management.
  • Objective assessment methods are needed for visual function after trauma.

Purpose of the Study:

  • To develop machine learning models using pattern visual evoked potentials (PVEPs).
  • To predict the stabilized visual acuity (VA) in patients with treated ocular trauma.
  • To evaluate the efficacy of different machine learning algorithms for this prediction task.

Main Methods:

  • Utilized data from 260 patients with unilateral ocular trauma.
  • Employed four machine learning algorithms: SVR, BYR, RFG, and XGBoost.
  • Trained models using ophthalmic parameters and PVEPs to predict best corrected visual acuity (BCVA) at least 6 months post-injury.

Main Results:

  • All models demonstrated high diagnostic performance (accuracy 0.7875–0.8133).
  • The XGBoost model achieved the lowest Mean Absolute Error (MAE) of 0.1598 logMAR.
  • XGBoost also yielded the lowest Root Mean Square Error (RMSE) of 0.2402 logMAR and highest accuracy of 0.8959.

Conclusions:

  • PVEP-driven machine learning models show significant promise for BCVA prediction.
  • These models can aid in the clinical evaluation of patients following ocular trauma.
  • The findings support the utility of PVEPs in assessing visual outcomes in trauma patients.