Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy.
Artificial intelligence
Critical View of Safety
Iatrogenic disease
Image classification
Laparoscopic cholecystectomy
Surgical safety
Journal
Surgical endoscopy
ISSN: 1432-2218
Titre abrégé: Surg Endosc
Pays: Germany
ID NLM: 8806653
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
26
03
2023
accepted:
19
07
2023
medline:
1
11
2023
pubmed:
12
8
2023
entrez:
11
8
2023
Statut:
ppublish
Résumé
The Critical View of Safety (CVS) was proposed in 1995 to prevent bile duct injury during laparoscopic cholecystectomy (LC). The achievement of CVS was evaluated subjectively. This study aimed to develop an artificial intelligence (AI) system to evaluate CVS scores in LC. AI software was developed to evaluate the achievement of CVS using an algorithm for image classification based on a deep convolutional neural network. Short clips of hepatocystic triangle dissection were converted from 72 LC videos, and 23,793 images were labeled for training data. The learning models were examined using metrics commonly used in machine learning. The mean values of precision, recall, F-measure, specificity, and overall accuracy for all the criteria of the best model were 0.971, 0.737, 0.832, 0.966, and 0.834, respectively. It took approximately 6 fps to obtain scores for a single image. Using the AI system, we successfully evaluated the achievement of the CVS criteria using still images and videos of hepatocystic triangle dissection in LC. This encourages surgeons to be aware of CVS and is expected to improve surgical safety.
Sections du résumé
BACKGROUND
BACKGROUND
The Critical View of Safety (CVS) was proposed in 1995 to prevent bile duct injury during laparoscopic cholecystectomy (LC). The achievement of CVS was evaluated subjectively. This study aimed to develop an artificial intelligence (AI) system to evaluate CVS scores in LC.
MATERIALS AND METHODS
METHODS
AI software was developed to evaluate the achievement of CVS using an algorithm for image classification based on a deep convolutional neural network. Short clips of hepatocystic triangle dissection were converted from 72 LC videos, and 23,793 images were labeled for training data. The learning models were examined using metrics commonly used in machine learning.
RESULTS
RESULTS
The mean values of precision, recall, F-measure, specificity, and overall accuracy for all the criteria of the best model were 0.971, 0.737, 0.832, 0.966, and 0.834, respectively. It took approximately 6 fps to obtain scores for a single image.
CONCLUSIONS
CONCLUSIONS
Using the AI system, we successfully evaluated the achievement of the CVS criteria using still images and videos of hepatocystic triangle dissection in LC. This encourages surgeons to be aware of CVS and is expected to improve surgical safety.
Identifiants
pubmed: 37567981
doi: 10.1007/s00464-023-10328-y
pii: 10.1007/s00464-023-10328-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8755-8763Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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