ArticleJournal of medical systems2019
Patient-Specific Simulation of Pneumoperitoneum for Laparoscopic Surgical Planning.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05039333 (Prospective Observational Clinical Study to Prove Safety and Effectiveness When Applying RUS™ Surgical Navigation, Endoscopic Imaging Treatment Planning Software, in Gastric Cancer Patients), which is not on this map. Cited by 5 papers.
What it found
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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.
Prospective Observational Clinical Study to Prove Safety and Effectiveness When Applying RUS™ Surgical Navigation, Endoscopic Imaging Treatment Planning Software, in Gastric Cancer Patients
Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Patient-specific role-aware port planning for a bedside-distributed multi-arm surgical robot: simulation and phantom validation.Journal of robotic surgery · 2026Article
- Deformable lung models for anatomical lung resections: The introduction of simulated reality for imaging guidance.JTCVS techniques · 2026Article
- Towards personalized trocar placement: assessment of patient-specific abdominal wall distension due to pneumoperitoneum.Journal of robotic surgery · 2025Article
- The technology of artificial pneumoperitoneum CT and its application in diagnosis of abdominal adhesion.Scientific reports · 2021Article
- Initial Report: A Novel Intraoperative Navigation System for Laparoscopic Liver Resection Using Real-Time Virtual Sonography.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Gas insufflation in laparoscopy deforms the abdomen and stretches the overlying skin. This limits the use of surgical image-guidance technologies and challenges the appropriate placement of trocars, which influences the operative ease and potential quality of laparoscopic surgery. This work describes the development of a platform that simulates pneumoperitoneum in a patient-specific manner, using preoperative CT scans as input data. This aims to provide a more realistic representation of the intraoperative scenario and guide trocar positioning to optimize the ergonomics of laparoscopic instrumentation. The simulation was developed by generating 3D reconstructions of insufflated and deflated porcine CT scans and simulating an artificial pneumoperitoneum on the deflated model. Simulation parameters were optimized by minimizing the discrepancy between the simulated pneumoperitoneum and the ground truth model extracted from insufflated porcine scans. Insufflation modeling in humans was investigated by correlating the simulation's output to real post-insufflation measurements obtained from patients in theatre. The simulation returned an average error of 7.26 mm and 10.5 mm in the most and least accurate datasets respectively. In context of the initial discrepancy without simulation (23.8 mm and 19.6 mm), the methods proposed here provide a significantly improved picture of the intraoperative scenario. The framework was also demonstrated capable of simulating pneumoperitoneum in humans. This study proposes a method for realistically simulating pneumoperitoneum to achieve optimal ergonomics during laparoscopy. Although further studies to validate the simulation in humans are needed, there is the opportunity to provide a more realistic, interactive simulation platform for future image-guided minimally invasive surgery.
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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.