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Updated: May 24, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Cataract-LMM Large-Scale Multi-Source Multi-Task Benchmark for Deep Learning in Surgical Video Analysis.

Mohammad Javad Ahmadi1, Iman Gandomi1, Parisa Abdi2

  • 1Applied Robotics and AI Solutions (ARAS), Faculties of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Scientific Data
|May 22, 2026
PubMed
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This summary is machine-generated.

Researchers created a diverse dataset of 3,000 cataract surgeries with detailed annotations. This resource aids in developing advanced artificial intelligence for surgical training and analysis.

Area of Science:

  • Ophthalmology
  • Computer Science
  • Medical Education

Background:

  • Developing generalizable deep learning models for computer-assisted surgery requires extensive, varied, and deeply annotated video datasets.
  • Current cataract surgery datasets lack the diversity and annotation depth needed for robust AI model training.

Purpose of the Study:

  • To introduce a comprehensive dataset of phacoemulsification cataract surgery videos to address limitations in existing resources.
  • To facilitate the development of advanced AI models for surgical workflow analysis, scene understanding, and competency-based training.

Main Methods:

  • Compiled a dataset of 3,000 phacoemulsification cataract surgery videos from two surgical centers, featuring surgeons of varying expertise.
  • Annotated videos with four layers: temporal surgical phases, instrument/structure segmentation, instrument-tissue interaction, and skill scores (ICO-OSCAR, GRASIS).

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  • Benchmarked deep learning models for workflow recognition, scene segmentation, interaction tracking, and skill assessment; established domain-adaptation baselines.

    • Main Results:

    • The dataset encompasses 3,000 videos with multi-layer annotations, capturing clinical and technical variability.
    • Deep learning models were benchmarked on tasks including workflow recognition and automated skill assessment.
    • Domain-adaptation baselines were established, demonstrating the dataset's utility for transfer learning.

    Conclusions:

    • The presented dataset, with its multi-source acquisitions and multi-layer annotations, is crucial for advancing AI in surgical education and analysis.
    • Facilitates research into generalizable multi-task models for surgical workflow, scene understanding, and competency assessment.
    • Enables the development of AI tools for improved surgical training and performance evaluation.