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Remote detection of the critical view of safety in pediatric laparoscopic cholecystectomy using artifitial
Sean Exequiel Olivieri1,2, Santiago Darrigran3, Enrique Petracchi1
1General Surgery, Hospital General de Agudos Dr. Cosme Argerich, Buenos Aires, Argentina.
Insights
Remote artificial intelligence (AI) successfully detected the critical view of safety (CVS) during pediatric laparoscopic cholecystectomy (LC). This AI-assisted approach proved feasible, safe, and consistent with expert surgeon evaluations, enhancing surgical safety in children.
Area of Science:
- Pediatric surgery
- Surgical safety
- Artificial intelligence in medicine
Background:
- Laparoscopic cholecystectomy (LC) is common in children, but bile duct injury is a serious risk.
- The critical view of safety (CVS) reduces this risk, yet its identification is subjective.
- Artificial intelligence (AI) shows promise for CVS detection in adults but hasn't been applied in pediatrics.
Purpose of the Study:
- To evaluate the feasibility and accuracy of remote AI-assisted CVS detection in pediatric LC.
- To assess the consistency of AI-driven CVS identification with expert surgical judgment.
- To explore the potential of AI to reduce subjectivity and improve access to advanced surgical tools in pediatric procedures.
Main Methods:
- A prospective, observational study tested an AI algorithm trained on adult LC cases in 50 pediatric patients.
- Surgeries were transmitted in real-time; the AI processed images to identify CVS structures.
- Two blinded expert surgeons independently assessed CVS presence, comparing their findings with the AI's.
Main Results:
- The study included 50 pediatric patients (mean BMI 29.3±5.8).
- The AI fully detected CVS in 37 cases and partially in 13.
- There was 100% agreement between the AI and expert surgeons' assessments, with no reported postoperative complications.
Conclusions:
- Remote AI-assisted CVS detection is feasible and safe in pediatric LC.
- The AI's performance was consistent with expert surgical evaluations.
- This technology holds potential for enhancing surgical safety and reducing subjectivity in pediatric procedures.
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.
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