Automated detection of steps in videos of strabismus surgery using deep learning.

Journal: BMC ophthalmology
PMID: 38853240

Abstract

BACKGROUND: Learning to perform strabismus surgery is an essential aspect of ophthalmologists' surgical training. Automated classification strategy for surgical steps can improve the effectiveness of training curricula and the efficient evaluation of residents' performance. To this end, we aimed to develop and validate a deep learning (DL) model for automated detecting strabismus surgery steps in the videos.

Authors

  • Ce Zheng
    Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Wen Li
  • Siying Wang
    Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Lu Ding Road # 355, Shanghai, 200000, China.
  • Haiyun Ye
    Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Kai Xu
    Department of Anesthesiology, Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huaian, China.
  • Wangyi Fang
    Department of Ophthalmology and Vision Science, Eye and ENT Hospital, Fudan University, Shanghai, People's Republic of China.
  • Yanli Dong
    Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Lu Ding Road # 355, Shanghai, 200000, China.
  • Zilei Wang
    Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China.
  • Tong Qiao
    Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.

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