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

A-eye: Automated 3D MRI segmentation and morphometric feature extraction for eye and orbit atlas construction.

PloS one·2026
Same author

Assessing intergrader variability in incomplete outer retinal atrophy (iRORA) grading.

The British journal of ophthalmology·2026
Same author

Current validation practice undermines surgical AI development.

ArXiv·2026
Same author

Drusen-specific dark adaptation profiles in intermediate age-related macular degeneration.

International journal of retina and vitreous·2026
Same author

Rapid, label-free cancer detection in fresh pancreatic tissue using deep learning and multispectral Mueller matrix polarimetry.

IEEE transactions on bio-medical engineering·2026
Same author

Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge.

Medical image analysis·2026

Related Experiment Video

Updated: Jun 28, 2025

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery
05:19

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Published on: December 1, 2023

1.0K

Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos.

Negin Ghamsarian1, Yosuf El-Shabrawi2, Sahar Nasirihaghighi3

  • 1Center for Artificial Intelligence in Medicine (CAIM), Department of Medicine, University of Bern, Bern, Switzerland.

Scientific Data
|April 12, 2024
PubMed
Summary

This study introduces the largest dataset for cataract surgery videos, crucial for advancing deep learning in surgical analysis and improving patient outcomes. The dataset supports better surgical workflow analysis and irregularity detection.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Author Spotlight: Enhancing Visual Outcomes in Cataract Surgery: A Novel Technique to Prevent Posterior Capsular Opacification Through IOL Rotation
04:59

Author Spotlight: Enhancing Visual Outcomes in Cataract Surgery: A Novel Technique to Prevent Posterior Capsular Opacification Through IOL Rotation

Published on: July 7, 2023

2.3K

Related Experiment Videos

Last Updated: Jun 28, 2025

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery
05:19

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Published on: December 1, 2023

1.0K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Author Spotlight: Enhancing Visual Outcomes in Cataract Surgery: A Novel Technique to Prevent Posterior Capsular Opacification Through IOL Rotation
04:59

Author Spotlight: Enhancing Visual Outcomes in Cataract Surgery: A Novel Technique to Prevent Posterior Capsular Opacification Through IOL Rotation

Published on: July 7, 2023

2.3K

Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Deep learning significantly advances computer-assisted surgery and video analysis.
  • Large-scale datasets and annotations are essential for developing deep-learning surgical technologies.
  • Surgical scene understanding and phase recognition are key for computer-assisted surgery and cataract surgery video assessment.

Purpose of the Study:

  • To present the largest cataract surgery video dataset for computerized surgical workflow analysis and post-operative irregularity detection.
  • To validate annotation quality using state-of-the-art neural networks for phase recognition and surgical scene segmentation.
  • To explore domain adaptation for instrument segmentation in cataract surgery.

Main Methods:

  • Developed the largest publicly available cataract surgery video dataset.
  • Benchmarked state-of-the-art neural networks for phase recognition and surgical scene segmentation.
  • Initiated research on domain adaptation for instrument segmentation using cross-domain evaluation.

Main Results:

  • The dataset supports diverse requisites for surgical workflow analysis and irregularity detection.
  • Annotation quality was validated through benchmarking various neural network architectures.
  • Initial findings on domain adaptation for instrument segmentation were evaluated.

Conclusions:

  • The presented dataset is a significant resource for advancing deep learning in cataract surgery analysis.
  • The findings validate the dataset's utility for surgical phase recognition, scene segmentation, and instrument detection.
  • Public availability of the dataset and annotations will foster further research in AI-driven surgical technologies.