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

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 misident...

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Autores principales: Endo, Yuichi, Tokuyasu, Tatsushi, Mori, Yasuhisa, Asai, Koji, Umezawa, Akiko, Kawamura, Masahiro, Fujinaga, Atsuro, Ejima, Aika, Kimura, Misako, Inomata, Masafumi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322759/
https://www.ncbi.nlm.nih.gov/pubmed/37365396
http://dx.doi.org/10.1007/s00464-023-10224-5
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author Endo, Yuichi
Tokuyasu, Tatsushi
Mori, Yasuhisa
Asai, Koji
Umezawa, Akiko
Kawamura, Masahiro
Fujinaga, Atsuro
Ejima, Aika
Kimura, Misako
Inomata, Masafumi
author_facet Endo, Yuichi
Tokuyasu, Tatsushi
Mori, Yasuhisa
Asai, Koji
Umezawa, Akiko
Kawamura, Masahiro
Fujinaga, Atsuro
Ejima, Aika
Kimura, Misako
Inomata, Masafumi
author_sort Endo, Yuichi
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-023-10224-5.
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spelling pubmed-103227592023-07-07 Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy Endo, Yuichi Tokuyasu, Tatsushi Mori, Yasuhisa Asai, Koji Umezawa, Akiko Kawamura, Masahiro Fujinaga, Atsuro Ejima, Aika Kimura, Misako Inomata, Masafumi Surg Endosc Dynamic Manuscript 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-023-10224-5. Springer US 2023-06-26 2023 /pmc/articles/PMC10322759/ /pubmed/37365396 http://dx.doi.org/10.1007/s00464-023-10224-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Dynamic Manuscript
Endo, Yuichi
Tokuyasu, Tatsushi
Mori, Yasuhisa
Asai, Koji
Umezawa, Akiko
Kawamura, Masahiro
Fujinaga, Atsuro
Ejima, Aika
Kimura, Misako
Inomata, Masafumi
Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
title Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
title_full Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
title_fullStr Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
title_full_unstemmed Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
title_short Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
title_sort impact of ai system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy
topic Dynamic Manuscript
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322759/
https://www.ncbi.nlm.nih.gov/pubmed/37365396
http://dx.doi.org/10.1007/s00464-023-10224-5
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