Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy

Yuichi Endo; Tatsushi Tokuyasu; Yasuhisa Mori; Koji Asai; Akiko Umezawa; Masahiro Kawamura; Atsuro Fujinaga; Aika Ejima; Misako Kimura; Masafumi Inomata

doi:10.1007/s00464-023-10224-5

Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy

Surg Endosc. 2023 Jul;37(7):5752-5759. doi: 10.1007/s00464-023-10224-5. Epub 2023 Jun 26.

Authors

Affiliations

¹ Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan. endo@oita-u.ac.jp.
² Department of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
³ Department of Surgery 1, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan.
⁴ Department of Surgery, Toho University Ohashi Medical Center, Tokyo, Japan.
⁵ Minimally Invasive Surgery Center, Yotsuya Medical Cube, Tokyo, Japan.
⁶ Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.

Abstract

Background: According to the National Clinical Database of Japan, the incidence of bile duct injury (BDI) during laparoscopic cholecystectomy has hovered around 0.4% for the last 10 years and has not declined. On the other hand, it has been found that about 60% of BDI occurrences are due to misidentifying anatomical landmarks. However, the authors developed an artificial intelligence (AI) system that gave intraoperative data to recognize the extrahepatic bile duct (EHBD), cystic duct (CD), inferior border of liver S4 (S4), and Rouviere sulcus (RS). The purpose of this research was to evaluate how the AI system affects landmark identification.

Methods: We prepared a 20-s intraoperative video before the serosal incision of Calot's triangle dissection and created a short video with landmarks overwritten by AI. The landmarks were defined as landmark (LM)-EHBD, LM-CD, LM-RS, and LM-S4. Four beginners and four experts were recruited as subjects. After viewing a 20-s intraoperative video, subjects annotated the LM-EHBD and LM-CD. Then, a short video is shown with the AI overwriting landmark instructions; if there is a change in each perspective, the annotation is changed. The subjects answered a three-point scale questionnaire to clarify whether the AI teaching data advanced their confidence in verifying the LM-RS and LM-S4. Four external evaluation committee members investigated the clinical importance.

Results: In 43 of 160 (26.9%) images, the subjects transformed their annotations. Annotation changes were primarily observed in the gallbladder line of the LM-EHBD and LM-CD, and 70% of these shifts were considered safer changes. The AI-based teaching data encouraged both beginners and experts to affirm the LM-RS and LM-S4.

Conclusion: The AI system provided significant awareness to beginners and experts and prompted them to identify anatomical landmarks linked to reducing BDI.

Keywords: Anatomical landmarks; And laparoscopic cholecystectomy (LC); Artificial intelligence (AI); Bile duct injury (BDI).

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Abdominal Injuries*
Artificial Intelligence
Bile Duct Diseases*
Bile Ducts / injuries
Bile Ducts, Extrahepatic* / surgery
Cholecystectomy, Laparoscopic* / adverse effects
Cholecystectomy, Laparoscopic* / methods
Cystic Duct
Humans