In the last five years, deep learning has attracted great interest in computer-assisted systems for Minimally Invasive Surgery. The straightforward accessibility to images in surgical interventions makes deep neural networks enormously powerful for solving classification problems in complex surgical scenarios. The objective of this work is to provide readers a survey on deep learning models applied to minimally invasive surgery, identifying the different architectures used depending on the application, the results achieved until now, and the publicly available surgical datasets that can be used for validating new studies. A total of 85 publications have been extracted from manual research from four databases (IEEE Xplorer, Springer Link, Science Direct, and ACM Digital Library). After analyzing all these studies, they have been classified into four applications: surgical image analysis, surgical task analysis, surgical skill assessment, and automation of surgical tasks. This work provides a technical description of these works and a comparison among them. Finally, promising research directions to advance in this field are identified.

Over the last years, minimally invasive surgery (MIS) has continuously improved due to new techniques and technologies. One of these novel techniques is hand-assisted laparoscopic surgery (HALS), where the surgeon inserts one hand through a small incision inside the abdominal cavity of the patient. As this kind of intervention only allows the use of one laparoscopic tool, the surgeon requires a deep collaboration with the assistant in order to coordinate their movements for performing a surgical maneuver. In this way, the replacement of a human assistant with a robot system specifically designed for HALS must include a natural human-machine interface and the capability for taking autonomous decisions. These features need the implementation of a model of the surgical protocol, a system capable of recognizing the corresponding surgical gestures made by the surgeon, and an autonomous task system that assists the surgeon without his direct intervention. This paper is focused on the design of a collaborative surgical robot that implements all the features previously described. This robot assistant has been validated by means of in vitro laparoscopic sutures on a HALS scenario with CISOBOT, a two-arm robotic platform designed and developed at the University of Malaga.

This paper presents a compliant motion control method for the robotic assistant ERM (Endoscopic Robotic Manipulator), designed and developed by the authors for handling the camera in laparoscopic s...

Endonasal surgery is a minimally invasive approach for the removal of pituitary tumors (sarcomas). In this type of procedure, the surgeon has to complete the surgical maneuvers for sarcoma resection with extreme precision, as there are many vital structures in this area. Therefore, the use of robots for this type of intervention could increase the success of the intervention by providing accurate movements. Research has focused on the development of teleoperated robots to handle a surgical instrument, including the use of virtual fixtures to delimit the working area. This paper aims to go a step further with a platform that includes a teleoperated robot and an autonomous robot dedicated to secondary tasks. In this way, the aim is to reduce the surgeon’s workload so that he can concentrate on his main task. Thus, the article focuses on the description and implementation of a navigator that coordinates both robots via a force/position control. Finally, both the navigation and control scheme were validated by in-vitro tests.

This paper presents a robotic assistant for helping surgeons in minimally invasive surgery. The system provides the direct control of the camera positioning inside the abdominal cavity, including teleoperation capabilities. This prototype does not re