Evidence map›Paper›PMID 39838147›Full record

ArticleSurgical endoscopy2025

Development of an artificial intelligence system to indicate intraoperative findings of scarring in laparoscopic cholecystectomy for cholecystitis.

Hiroki Orimoto, Teijiro Hirashita, Subaru Ikeda, Shota Amano, Masahiro Kawamura, Yoko Kawano, Hiroomi Takayama, Takashi Masuda, Yuichi Endo, Yusuke Matsunobu and 3 more

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In one paragraph

Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. 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

13 authors.

Hiroki OrimotoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan. orihiro@oita-u.ac.jp.ORCID 0009-0009-1015-2343
Teijiro HirashitaDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.
Subaru IkedaDepartment of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Shota AmanoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.
Masahiro KawamuraDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.
Yoko KawanoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.
Hiroomi TakayamaDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.
Takashi MasudaDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.
Yuichi EndoDepartment of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, 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, 1-1 Hasama-Machi, Yufu, Oita, 879-5593, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe surgical difficulty of laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) and the risk of bile duct injury (BDI) depend on the degree of fibrosis and scarring caused by inflammation; therefore, understanding these intraoperative findings is crucial to preventing BDI. Scarring makes it particularly difficult to perform safely and increases the BDI risk. This study aimed to develop an artificial intelligence (AI) system to indicate intraoperative findings of scarring in LC for AC. MATERIALS AND

methodsAn AI system was developed to detect scarred areas using an algorithm for semantic segmentation based on deep learning. The training dataset consisted of 2025 images extracted from LC videos of 21 cases with AC. External evaluation committees (EEC) evaluated the AI system on 20 cases of untrained data from other centers. EECs evaluated the accuracy in identifying the scarred area and the usefulness of the AI system, which were assessed based on annotation and a 5-point Likert-scale questionnaire.

resultsThe average DICE coefficient for scarred areas between AI detection and EEC annotation was 0.612. The EEC's average detection accuracy on the Likert scale was 3.98 ± 0.76. AI systems were rated as relatively useful for both clinical and educational applications.

conclusionWe developed an AI system to detect scarred areas in LC for AC. Since scarring increases the surgical difficulty, this AI system has the potential to reduce BDI.

Indexed as

Artificial IntelligenceBile DuctsCholecystectomy, LaparoscopicCholecystitis, AcuteCicatrixIntraoperative ComplicationsAdultAgedAlgorithmsDeep LearningFemaleHumansMaleMiddle AgedAcute cholecystitisArtificial intelligence systemBile duct injuryLaparoscopic cholecystectomySurgical difficulty

Identifiers

PMID39838147
PMCPMC11794413

What OpenQuestion holds

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LicenceCC BY
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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.