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<p>Foundation models in artificial intelligence are revolutionizing healthcare by utilizing large-scale unlabelled data for pretraining. However, their intraoperative applications remain underexplored owing to limited surgical data and the challenges of real-time deployment. Here we show the development of the ophthalmic video foundation model (OVFM), designed for microscopic ophthalmic surgical recognition and navigation. Leveraging a self-supervised video transformer structure and trained on an ophthalmic video dataset comprising 1.1 million clips across 144 surgical types, OVFM learns the spatiotemporal motion features of ophthalmic procedures. We demonstrate OVFM’s superior performance across seven downstream tasks. To enable real-time use, we applied knowledge distillation, reducing the model’s size while retaining its accuracy, which allows for deployment on surgical microscope units. In cataract surgeries performed by ten surgeons on wet-lab porcine eyes, the OVFM-powered system enhanced surgical performance and reduced skill gaps, demonstrating notable potential for real-time, intraoperative applications across various surgical fields.</p>

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An ophthalmic video foundation model for surgical recognition and navigation with wet-lab porcine eye validation

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Puxun Tu,
  • Ce Zheng,
  • Xiaoling Xie,
  • Jiao Lv,
  • Meng Xie,
  • Jinming Guo,
  • Shengjie Yin,
  • Kunliang Qiu,
  • Yue Wei,
  • Chongyang Wang,
  • Jingfeng Cai,
  • Wei Mi,
  • Yafu Wang,
  • Xiao Zhang,
  • Danba Jiachu,
  • Kun Peng,
  • Wanqi Zhang,
  • Fengfeng Tong,
  • Huiying Chu,
  • Peiquan Zhao,
  • Mingzhi Zhang,
  • Xiaojun Chen

摘要

Foundation models in artificial intelligence are revolutionizing healthcare by utilizing large-scale unlabelled data for pretraining. However, their intraoperative applications remain underexplored owing to limited surgical data and the challenges of real-time deployment. Here we show the development of the ophthalmic video foundation model (OVFM), designed for microscopic ophthalmic surgical recognition and navigation. Leveraging a self-supervised video transformer structure and trained on an ophthalmic video dataset comprising 1.1 million clips across 144 surgical types, OVFM learns the spatiotemporal motion features of ophthalmic procedures. We demonstrate OVFM’s superior performance across seven downstream tasks. To enable real-time use, we applied knowledge distillation, reducing the model’s size while retaining its accuracy, which allows for deployment on surgical microscope units. In cataract surgeries performed by ten surgeons on wet-lab porcine eyes, the OVFM-powered system enhanced surgical performance and reduced skill gaps, demonstrating notable potential for real-time, intraoperative applications across various surgical fields.