Development of a Novel Artificial Intelligence System for Laparoscopic Hepatectomy.
AI
Artificial intelligence
laparoscopic hepatectomy
Journal
Anticancer research
ISSN: 1791-7530
Titre abrégé: Anticancer Res
Pays: Greece
ID NLM: 8102988
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
08
08
2023
revised:
21
08
2023
accepted:
27
09
2023
medline:
2
11
2023
pubmed:
1
11
2023
entrez:
1
11
2023
Statut:
ppublish
Résumé
Laparoscopic hepatectomy (LH) requires accurate visualization and appropriate handling of hepatic veins and the Glissonean pedicle that suddenly appear during liver dissection. Failure to recognize these structures can cause injury, resulting in severe bleeding and bile leakage. This study aimed to develop a novel artificial intelligence (AI) system that assists in the visual recognition and color presentation of tubular structures to correct the recognition gap among surgeons. Annotations were performed on over 350 video frames capturing LH, after which a deep learning model was developed. The performance of the AI was evaluated quantitatively using intersection over union (IoU) and Dice coefficients, as well as qualitatively using a two-item questionnaire on sensitivity and misrecognition completed by 10 hepatobiliary surgeons. The usefulness of AI in medical education was qualitatively evaluated by 10 medical students and residents. The AI model was able to individually recognize and colorize hepatic veins and the Glissonean pedicle in real time. The IoU and Dice coefficients were 0.42 and 0.53, respectively. Surgeons provided a mean sensitivity score of 4.24±0.89 (from 1 to 5; Excellent) and a mean misrecognition score of 0.12±0.33 (from 0 to 4; Fail). Medical students and residents assessed the AI to be very useful (mean usefulness score, 1.86±0.35; from 0 to 2; Excellent). The novel AI presented was able to assist surgeons in the intraoperative recognition of microstructures and address the recognition gap among surgeons to ensure a safer and more accurate LH.
Sections du résumé
BACKGROUND/AIM
OBJECTIVE
Laparoscopic hepatectomy (LH) requires accurate visualization and appropriate handling of hepatic veins and the Glissonean pedicle that suddenly appear during liver dissection. Failure to recognize these structures can cause injury, resulting in severe bleeding and bile leakage. This study aimed to develop a novel artificial intelligence (AI) system that assists in the visual recognition and color presentation of tubular structures to correct the recognition gap among surgeons.
PATIENTS AND METHODS
METHODS
Annotations were performed on over 350 video frames capturing LH, after which a deep learning model was developed. The performance of the AI was evaluated quantitatively using intersection over union (IoU) and Dice coefficients, as well as qualitatively using a two-item questionnaire on sensitivity and misrecognition completed by 10 hepatobiliary surgeons. The usefulness of AI in medical education was qualitatively evaluated by 10 medical students and residents.
RESULTS
RESULTS
The AI model was able to individually recognize and colorize hepatic veins and the Glissonean pedicle in real time. The IoU and Dice coefficients were 0.42 and 0.53, respectively. Surgeons provided a mean sensitivity score of 4.24±0.89 (from 1 to 5; Excellent) and a mean misrecognition score of 0.12±0.33 (from 0 to 4; Fail). Medical students and residents assessed the AI to be very useful (mean usefulness score, 1.86±0.35; from 0 to 2; Excellent).
CONCLUSION
CONCLUSIONS
The novel AI presented was able to assist surgeons in the intraoperative recognition of microstructures and address the recognition gap among surgeons to ensure a safer and more accurate LH.
Identifiants
pubmed: 37909965
pii: 43/11/5235
doi: 10.21873/anticanres.16725
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5235-5243Informations de copyright
Copyright © 2023 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.