Evidence map›Paper›PMID 37567981›Full record

ArticleSurgical endoscopy2023

Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy.

Masahiro Kawamura, Yuichi Endo, Atsuro Fujinaga, Hiroki Orimoto, Shota Amano, Takahide Kawasaki, Yoko Kawano, Takashi Masuda, Teijiro Hirashita, Misako Kimura and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
7.0field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 2 institutions in 1 country.

Masahiro KawamuraDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan. kawamura@oita-u.ac.jp.ORCID 0009-0008-1964-8882
Yuichi EndoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Atsuro FujinagaDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Hiroki OrimotoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Shota AmanoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Takahide KawasakiDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Yoko KawanoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Takashi MasudaDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Teijiro HirashitaDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Misako KimuraDepartment of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Aika EjimaDepartment of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Yusuke MatsunobuDepartment of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Ken'ichi ShinozukaDepartment of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Tatsushi TokuyasuDepartment of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Masafumi InomataDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan.
Oita University · JPFukuoka Institute of Technology · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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

methodsAI 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.

resultsThe 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.

conclusionsUsing 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.

Indexed as

Cholecystectomy, LaparoscopicSurgeonsArtificial IntelligenceHumansVideo RecordingVideotape RecordingArtificial intelligenceCritical View of SafetyIatrogenic diseaseImage classificationLaparoscopic cholecystectomySurgical safety

Identifiers

PMID37567981
OpenAlexW4385753816

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.