Purpose: The aim of this study is to propose a model that simulates patient-specific anatomical changes resulting from pneumoperitoneum, using preoperative data as input. The framework can assist the surgeon through a real-time visualisation and interaction with the model. Such could further facilitate surgical planning preoperatively, by defining a surgical strategy, and intraoperatively to estimate port positions.
Methods: The biomechanical model that simulates pneumoperitoneum was implemented within the GPU-accelerated NVIDIA FleX position-based dynamics framework. Datasets of multiple porcine subjects before and after abdominal insufflation were used to generate, calibrate and validate the model. The feasibility of modelling pneumoperitoneum in human subjects was assessed by comparing distances between specific landmarks from a patient abdominal wall, to the same landmark measurements on the simulated model.
Results: The calibration of simulation parameters resulted in a successful estimation of an optimal set parameters. A correspondence between the simulation pressure parameter and the experimental insufflation pressure was determined. The simulation of pneumoperitoneum in a porcine subject resulted in a mean Hausdorff distance error of 5-6 mm. Feasibility of modelling pneumoperitoneum in humans was successfully demonstrated.
Conclusion: Simulation of pneumoperitoneum provides an accurate subject-specific 3D model of the inflated abdomen, which is a more realistic representation of the intraoperative scenario when compared to preoperative imaging alone. The simulation results in a stable and interactive framework that performs in real time, and supports patient-specific data, which can assist in surgical planning.
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http://dx.doi.org/10.1007/s11548-019-01924-2 | DOI Listing |
Int J Comput Assist Radiol Surg
December 2024
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Purpose: In laparoscopic liver surgery, registering preoperative CT-extracted 3D models with intraoperative laparoscopic video reconstructions of the liver surface can help surgeons predict critical liver anatomy. However, the registration process is challenged by non-rigid deformation of the organ due to intraoperative pneumoperitoneum pressure, partial visibility of the liver surface, and surface reconstruction noise.
Methods: First, we learn point-by-point descriptors and encode location information to alleviate the limitations of descriptors in location perception.
J Formos Med Assoc
December 2024
Division of Colorectal Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, 100225, Taiwan. Electronic address:
Background: Surgical smoke generated by energy devices poses health risks to medical staff. During laparoscopic surgery, the smoke aggregating around the camera obstructs the visual field, forcing surgeons to interrupt surgery, and may increase surgical risk. We propose a proximal smoke evacuation method to improve surgical quality by effectively eliminating surgical smoke.
View Article and Find Full Text PDFRev Col Bras Cir
November 2024
- Faculdade de Ciências da Saúde de Barretos Dr. Paulo Prata - FACISB, Medicina - Barretos - SP - Brasil.
Introduction: All forms of access to the peritoneal cavity in laparoscopy could damage intra-abdominal structures. Currently, ultrasound (USG) is being used in several procedures to guide needles: breast biopsy, central venous access puncture, anesthetic nerve blocks, etc. Therefore, this research seeks to verify the feasibility and viability of performing pneumoperitoneum using USG-guided puncture in a pilot study using a porcine model.
View Article and Find Full Text PDFClin Imaging
January 2025
Keck School of Medicine of University of Southern California, Los Angeles, CA, United States of America; Department of Radiology, Cedars Sinai Hospital, Los Angeles, CA, United States of America. Electronic address:
Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependability of its radiological diagnoses. The current advances in AI technologies have changed how radiologists and clinicians make the diagnoses of pathological conditions such as aneurysms, hemorrhages, pneumothorax, pneumoperitoneum, and particularly fractures.
View Article and Find Full Text PDFAsian J Surg
November 2024
Department of Obstetrics and Gynecology, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku, Tokyo, 160-0023, Japan.
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