Automatic recognition of surgical phase of robot-assisted radical prostatectomy based on artificial intelligence deep-learning model and its application in surgical skill evaluation: a joint study of 18 medical education centers.

Journal: Surgical endoscopy
Published Date: Jul 10, 2025

Abstract

BACKGROUND: Surgical proficiency influences surgical quality and patient outcomes in robot-assisted radical prostatectomy (RARP). Manual video evaluations are labor-intensive and lack standardized objective metrics. Herein, we aimed to develop an artificial intelligence (AI) deep-learning model that can identify the surgical phases in RARP videos and create a parameter-based scoring system to distinguish experts from novice surgeons based on the results of the AI model.

Authors

  • Xue Zhao
    Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China; Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Shin Takenaka
    Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
  • Shuntaro Iuchi
    Department of Urology, Chiba University Graduate School of Medicine, Chiba, 260-8670, Japan.
  • Daichi Kitaguchi
    Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan.
  • Masashi Wakabayashi
    Biostatistics Division, Center for Research Administration and Support, National Cancer Center, Tokyo, Japan.
  • Kodai Sato
    Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuoku, Chiba-shi, Chiba, 260-8670, Japan.
  • Shintaro Arakaki
    Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan.
  • Kimimasa Sasaki
    Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan; Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan.
  • Norihito Kosugi
    Surgical Device Innovation.
  • Nobushige Takeshita
    Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Department of Urology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan; Department of Urology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
  • Nobuyoshi Takeshita
    Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan. ntakeshi@east.ncc.go.jp.
  • Shinichi Sakamoto
    Department of Urology, Chiba University Hospital, Chiba, Japan.
  • Tomohiko Ichikawa
    Department of Urology, Chiba University Hospital, Chiba, Japan.
  • Masaaki Ito
    Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, 277-8577, Japan.

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