Pregnancy following robot-assisted laparoscopic partial cystectomy and gonadotropin-releasing hormone agonist treatment within three months in an infertile woman with bladder endometriosis

Robotic Surgery for Bladder Endometriosis: A Systematic Review and Approach

INTRODUCTION

Women with bladder endometriosis often present with more advanced stages of endometriosis. Robotic surgery has emerged as a promising approach to the management of bladder endometriosis. This systematic review aims to analyze the current literature on robotic surgery for bladder endometriosis and describe our systematic approach to surgical treatment.

METHODS

This review followed the PRISMA guidelines, which ensured a comprehensive and transparent approach to selecting and evaluating relevant studies. We conducted a thorough literature search to identify studies that investigated the use of robotic surgery for bladder endometriosis. Relevant databases were searched, and inclusion and exclusion criteria were applied to select eligible studies. Data extraction and analysis were performed to assess the outcomes and effectiveness of robotic surgery for the treatment of bladder endometriosis.

RESULTS

We did not find any randomized clinical trials with the use of robotics in the treatment of bladder endometriosis. We found only two retrospective studies comparing robotic surgery with laparoscopy, and another retrospective study comparing robotic surgery, laparoscopy, and laparotomy in the treatment of bladder endometriosis. All the other 12 studies were solely case reports. Despite the lack of robust evidence in the literature, the studies demonstrated that robotic surgery is feasible and is associated with reduced postoperative pain, shorter hospital stays, and faster recovery.

CONCLUSIONS

The utilization of robotic technology is a promising option for the surgical management of bladder endometriosis. We advocate a surgical systematic approach for the robotic treatment of bladder endometriosis. Robotic technology, with its 3D vision, instrumental degrees of freedom, and precision, particularly in suturing, may provide potential benefits over traditional laparoscopy.

Pain after laparoscopic surgery: Focus on shoulder-tip pain after gynecological laparoscopic surgery

Laparoscopy, one of minimally invasive procedures, is a commonly used procedure in diagnosis and management of various kinds of clinical problems, including gynecologic organ-related diseases. Compared with conventional exploratory laparotomy, the benefits of laparoscopic surgery include reduction of surgical wound, decreasing in postoperative pain, shortening hospital stay, rapid recovery, and a better cosmetic result. However, there are still up to 80% of patients after laparoscopic surgery complaining of high levels of pain and needing pain relief. Postlaparoscopic pain can be separated into distinct causes, such as surgical trauma- or incision wound-associated inflammatory change, and pneumoperitoneum (carbon dioxide [CO2])-related morphological and biochemical changes of peritoneum and diaphragm. The latter is secondary to irritation, stretching, and foreign body stimulation, leading to phrenic neuropraxia and subsequent shoulder-tip pain (STP). STP is the most typical unpleasant experience of patients after laparoscopic surgery. There are at least 11 strategies available to attempt to decrease postlaparoscopic STP, including (1) the use of an alternative insufflating gas in place of CO2, (2) the use of low-pressure pneumoperitoneum in place of standard-pressure pneumoperitoneum, (3) the use of warmed or warmed and humidified CO2, (4) gasless laparoscopy, (5) subdiaphragmatic intraperitoneal anesthesia, (6) local intraperitoneal anesthesia, (7) actively expelling out of gas, (8) intraperitoneal drainage, (9) fluid instillation, (10) pulmonary recruitment maneuvers, and (11) others and combination. The present article is limited in discussing postlaparoscopic STP. We extensively review published articles to provide a better strategy to reduce postlaparoscopic STP.

Artificial intelligence in reproductive medicine

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.