Artificial intelligence automated surgical phases recognition in intraoperative videos of laparoscopic pancreatoduodenectomy.

Journal: Surgical endoscopy
PMID: 38958719

Abstract

BACKGROUND: Laparoscopic pancreatoduodenectomy (LPD) is one of the most challenging operations and has a long learning curve. Artificial intelligence (AI) automated surgical phase recognition in intraoperative videos has many potential applications in surgical education, helping shorten the learning curve, but no study has made this breakthrough in LPD. Herein, we aimed to build AI models to recognize the surgical phase in LPD and explore the performance characteristics of AI models.

Authors

  • Jiaying You
    Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada; George & Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Manitoba, Canada.
  • He Cai
  • Yuxian Wang
    School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
  • Ang Bian
    College of Computer Science, Sichuan University, Chengdu, China.
  • Ke Cheng
    School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
  • Lingwei Meng
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Pan Gao
    College of Information Science and Technology, Shihezi University, Shihezi 832003, China.
  • Sirui Chen
    School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
  • Yunqiang Cai
    Department of Pancreatic Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041 China.
  • Bing Peng
    Department of Pancreatic Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan 610041 China.

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