With advances in minimal invasive surgery, robotic surgery has become the widespread approach for surgical staging of endometrial cancer in the obese population. This study aimed to evaluate safety and surgical outcomes of robotic surgery in the morbidly obese and extremely morbidly obese patients with endometrial cancer. Retrospective cohort study. A total of 391 obese women undergoing robotic-assisted surgical staging were identified and included in the study. Surgical outcomes for obese patients (BMI > 30 kg/m) who underwent surgical staging between 2011 and 2019 were retrospectively collected. Preoperative characteristics, perioperative outcomes and postoperative complications were analyzed among the categories of obesity (BMI ≥ 30-34.9 kg/m, ≥ 35-39.9 kg/m, ≥ 40-49.9 kg/m) including the extremely morbid obese (BMI > 50 kg/m). Comparative analysis revealed a higher percentage of postoperative complications with increasing BMI, although the results were not statistically significant. Postoperative complications are observed at higher rates among women with increasing BMI.
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http://dx.doi.org/10.1007/s11701-021-01277-6 | DOI Listing |
J Craniofac Surg
January 2025
Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, School of Medicine, Shanghai Jiao Tong University.
Background: This paper presents the authors' team's research on a craniofacial surgical robot developed in China. Initiated in 2011 with government funding, the craniofacial surgical robot project was officially launched in Shanghai, developed jointly by the Ninth People's Hospital affiliated with Shanghai Jiao Tong University School of Medicine and the Shanghai Jiao Tong University medical-engineering team. Currently, based on multiple rounds of model surgeries, animal experiments, and clinical trials, our team is applying for approval as a Class III medical device from the National Medical Products Administration (NMPA).
View Article and Find Full Text PDFSurg Today
January 2025
Department of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Purpose: The double-flap technique (DFT) is an anti-reflux reconstruction procedure performed after proximal gastrectomy (PG), but its complexity and high incidence of anastomotic stenosis are problematic. We conducted this study to demonstrate the efficacy of robot-assisted DFT, with refinements, to address these issues.
Methods: Surgical outcomes were compared between the following procedures modified over time at our institution: conventional open DFT (group O, n = 16); early robotic DFT (group RE, n = 19), which follows the conventional open PG approach; and late robotic DFT (group RL, n = 21), which incorporates refinements to the early robotic DFT technique by exploiting more of the robotic capabilities available.
J Robot Surg
January 2025
Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510230, Guangdong, China.
This study applied cumulative sum (CUSUM) analysis to evaluate trends in operative time and blood loss, It aims to identify key milestones in mastering extraperitoneal single-site robotic-assisted radical prostatectomy (ss-RARP). A cohort of 100 patients who underwent ss-RARP, performed by a single surgeon at the First Affiliated Hospital of Guangzhou Medical University between March 2021 and June 2023, was retrospectively analyzed. To evaluate the learning curve, the CUSUM (Cumulative Sum Control Chart) technique was applied, revealing the progression and variability over time.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
Stereotactic radiosurgery (SRS) and radiotherapy (SRT) have gained prominence as both adjuvant and primary treatment options for patients with skull base tumors that are either inoperable or present as residual or recurrent lesions post-surgery. The object of the current study is to evaluate the safety and efficacy of robotic-assisted SRS and SRT across various skull base pathologies. The study was conducted under PRISMA guidelines and involved a comprehensive evaluation of databases, including PubMed, Scopus, Embase, Web-of-Science, and the Cochrane Library.
View Article and Find Full Text PDFCurr Res Transl Med
January 2025
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
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