ArticleWorld journal of pediatric surgery2026
Remote detection of the critical view of safety in pediatric laparoscopic cholecystectomy using artifitial intelligence.
Article in World journal of pediatric surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Remote detection of the critical view of safety in pediatric laparoscopic cholecystectomy using artifitial intelligence.World journal of pediatric surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Laparoscopic cholecystectomy (LC) is increasingly performed in pediatric patients. Bile duct injury remains one of its most serious complications. The critical view of safety (CVS) aims to reduce this risk, but its identification is subjective. Artificial intelligence (AI) has shown promise in adult surgery for CVS detection but it has not been applied in pediatrics. Remote implementation of AI could reduce subjectivity and improve access to advanced tools. Methods: A prospective, observational, cross-sectional, single-blind study was conducted between May and August 2025. An AI algorithm trained with 346 validated adult LC cases was tested live in 50 pediatric patients. Surgeries were transmitted in real time via teleconferencing to a second center where the algorithm processed the surgical image and identified CVS structures. Two expert surgeons, blinded to the algorithm, assessed CVS presence independently. Results: A total of 50 patients (38 females and 12 males were classified as middle childhood to early adolescence) were included. The mean body mass index was 29.3±5.8. CVS was detected fully in 37 cases. In 13 cases, one or more elements were absent. Agreement between the algorithm and surgeons assessments was 100%. No postoperative complications were reported. Conclusion: Remote AI-assisted CVS detection in pediatric LC is feasible, safe and consistent with expert evaluation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.