Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy.

Journal: Surgical endoscopy
Published Date: Mar 9, 2022

Abstract

BACKGROUND: Surgical process modeling automatically identifies surgical phases, and further improvement in recognition accuracy is expected with deep learning. Surgical tool or time series information has been used to improve the recognition accuracy of a model. However, it is difficult to collect this information continuously intraoperatively. The present study aimed to develop a deep convolution neural network (CNN) model that correctly identifies the surgical phase during laparoscopic cholecystectomy (LC).

Authors

  • Ken'ichi Shinozuka
    Faculty of Information Engineering, Department of Information and Systems Engineering, Fukuoka Institute of Technology, 1-30-1 Wajiro higashi, Higashi-ku, Fukuoka, Fukuoka, 811-0295, Japan.
  • Sayaka Turuda
    Faculty of Information Engineering, Department of Information and Systems Engineering, Fukuoka Institute of Technology, 1-30-1 Wajiro higashi, Higashi-ku, Fukuoka, Fukuoka, 811-0295, Japan.
  • Atsuro Fujinaga
    Faculty of Medicine, Department of Gastroenterological and Pediatric Surgery, Oita University, Oita, Japan.
  • Hiroaki Nakanuma
    Faculty of Medicine, Department of Gastroenterological and Pediatric Surgery, Oita University, Oita, Japan.
  • Masahiro Kawamura
    Faculty of Medicine, Department of Gastroenterological and Pediatric Surgery, Oita University, Oita, Japan.
  • Yusuke Matsunobu
    Faculty of Information Engineering, Department of Information and Systems Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka-City, Fukuoka, 811-0295, Japan.
  • Yuki Tanaka
    Department of Orthopedic Surgery, Tsuruoka Municipal Shonai Hospital, 4-20 Izumi-machi, Tsuruoka-shi, Yamagata, 997-8515, Japan.
  • Toshiya Kamiyama
    Customer Solutions Development, Platform Technology, Olympus Technologies Asia, Olympus Corporation, 2-3 Kuboyama-cho, Hachioji-City, Tokyo, 192-8512, Japan.
  • Kohei Ebe
    Customer Solutions Development, Platform Technology, Olympus Technologies Asia, Olympus Corporation, 2-3 Kuboyama-cho, Hachioji-City, Tokyo, 192-8512, Japan.
  • Yuichi Endo
    Faculty of Medicine, Department of Gastroenterological and Pediatric Surgery, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu-City, Oita, 879-5593, Japan.
  • Tsuyoshi Etoh
    Faculty of Medicine, Department of Gastroenterological and Pediatric Surgery, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu-City, Oita, 879-5593, Japan.
  • Masafumi Inomata
    Faculty of Medicine, Department of Gastroenterological and Pediatric Surgery, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu-City, Oita, 879-5593, Japan.
  • Tatsushi Tokuyasu
    Faculty of Information Engineering, Department of Information and Systems Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka-City, Fukuoka, 811-0295, Japan. tokuyasu@fit.ac.jp.

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