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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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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. |
format | Online Article Text |
id | pubmed-10322759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
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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