Esophagectomy for cancer of the esophagus is increasingly performed using minimally invasive techniques. After the introduction of minimally invasive esophagectomy (MIE) in the early 1990's, robotic-assisted techniques followed after the turn of the millennium. The advent of robotic platforms has allowed the development of robotic-assisted minimally invasive esophagectomy (RAMIE) over the past 15 years. Although recent trials have shown superior peri-operative morbidity and quality of life compared to open esophagectomy, no randomized trials have compared RAMIE to conventional MIE. This paper summarizes the current literature on RAMIE and provides an overview of expected future developments in robotic surgery.
Video-assisted thoracic surgery (VATS) is a minimally invasive approach for treating early-stage non-small-cell lung cancer. Optimal trocar placement during VATS ensures comprehensive access to the thoracic cavity, provides a panoramic endoscopic view, and prevents instrument crowding. While established principles such as the Baseball Diamond Principle (BDP) and Triangle Target Principle (TTP) exist, surgeons mainly rely on experience and patient-specific anatomy for trocar placement, potentially leading to sub-optimal surgical plans that increase operative time and fatigue.
Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.
Despite the benefits of minimally invasive surgery, interventions such as laparoscopic liver surgery present unique challenges, like the significant anatomical differences between preoperative images and intraoperative scenes due to pneumoperitoneum, patient pose, and organ manipulation by surgical instruments. To address these challenges, a method for intraoperative three-dimensional reconstruction of the surgical scene, including vessels and tumors, without altering the surgical workflow, is proposed. The technique combines neural radiance field reconstructions from tracked laparoscopic videos with ultrasound three-dimensional compounding.
The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework is proposed for markerless estimation of the 6DoF pose of surgical instruments.
Department of Neurosurgery and Chinese Evidence-Based Medicine Centre and Cochrane China Centre and MAGIC China Centre and IDEAL China Centre, West China Hospital, Sichuan University, Chengdu, China.
Background: Surgical interventions for spontaneous supratentorial intracerebral haemorrhage (ICH) include conventional craniotomy (CC), decompressive craniectomy (DC), and minimally invasive surgery (MIS), with the latter encompassing endoscopic surgery (ES) and minimally invasive puncture surgery (MIPS). However, the superiority of surgery over conservative medical treatment (CMT) and the comparative benefits of different surgical procedures remain unclear. We aimed to evaluate the efficacy and safety of various surgical interventions for treating ICH.
