Abstract

The relatively recent adoption of Endoscopic Sleeve Gastroplasty (ESG) amongst obese patients has gained approval within the surgical community due to its notable benefits, including significant weight loss, safety, feasibility, repeatability, and potential reversibility. However, despite its promising clinical outcomes and reduced invasiveness, there is still a lack of standardised procedures for performing ESG. Multiple suture patterns and stitching methods have been proposed over time, yet rational tools to quantify and compare their effects on gastric tissues are absent. To address this gap, this study proposed a computational approach. The research involved a case study analyzing three distinct suture patterns (C-shaped, U-shaped and Z-shaped) using a patient-specific computational stomach model generated from magnetic resonance imaging. Simulations mimicked food intake by placing wire features in the intragastric cavity to replicate sutures, followed by applying a linearly increasing internal pressure up to 15mmHg. The outcomes facilitated comparisons between suture configurations based on pressure-volume behaviours and the distribution of maximum stress on biological tissues, revealing the U-shaped as the more effective in terms of volume reduction, even if with reduced elongation strains and increased tissues stresses, whereas the Z-shaped is responsible of the greatest stomach shortness after ESG. In summary, computational biomechanics methods serve as potent tools in clinical and surgical settings, offering insights into aspects that are challenging to explore in vivo, such as tissue elongation and stress. These methods allow for mechanical comparisons between different configurations, although they might not encompass crucial clinical outcomes.

Full Text
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