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.

Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to reduce the surgeon workload through the automation of secondary or auxiliary tasks, which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time. One of the most important secondary tasks is the endoscopic camera guidance, whose automation would allow the surgeon to be concentrated on handling the surgical instruments. This paper proposes a novel autonomous camera guidance approach for laparoscopic surgery. It is based on learning from demonstration (LfD), which has demonstrated its feasibility to transfer knowledge from humans to robots by means of multiple expert showings. The proposed approach has been validated using an experimental surgical robotic platform to perform peg transferring, a typical task that is used to train human skills in laparoscopic surgery. The results show that camera guidance can be easily trained by a surgeon for a particular task. Later, it can be autonomously reproduced in a similar way to one carried out by a human. Therefore, the results demonstrate that the use of learning from demonstration is a suitable method to perform autonomous camera guidance in collaborative surgical robotic platforms.

The design of safe stimulation protocols for functional electrostimulation requires knowledge of the “maximum reversible charge injection capacity” of the implantable microelectrodes. One of the main difficulties encountered in characterizing such microelectrodes is the calculation of the access voltage V_a. This paper proposes a method to calculate V_a that does not require prior knowledge of the overpotential terms and of the electrolyte (or excitable tissue) resistance, which is an advantage for in vivo electrochemical characterization of microelectrodes. To validate this method, we compare the calculated results with those obtained from conventional methods for characterizing three flexible platinum microelectrodes by cyclic voltammetry and voltage transient measurements. This paper presents the experimental setup, the required instrumentation, and the signal processing.

This paper describes the mechanical design and a cognition system for a novel concept-of-camera robotic assistant. The system combines the advantages of intra-abdominal devices and autonomous camera navigation. The robotic assistant is composed of a magnetic intra-abdominal camera robot with two internal cable-driven degrees of freedom and an external robot that handles an external magnet. The intelligence of the robot is implemented in a cognitive architecture based on a long-term memory that stores the robot's knowledge and learning capabilities to improve the robot's behavior. The navigation strategy combines a reactive control based on instrument tracking and a proactive control based on predefined behaviors, depending on the actual state of the task. The robot's learning capabilities include a semantic customization, to adapt the camera's behavior to the surgeon's preferences, and reinforcement learning, to improve the camera navigation strategy. This paper details both the hardware implementation of the system and the software implementation in a robotic operating system architecture. The cognition system and the performance of the cable-driven mechanism have been validated with a set of in vitro experiments. Moreover, the camera robot has been evaluated through an in-vivo experiment in a pig.

The development of glucose concentration sensors by means of microwave planar resonant technology is an active field attracting considerable attention from the scientific community. Although showing promising results, the current experimental sensors are facing some fundamental challenges. Among them, the most critical one seems to be the selectivity of glucose concentration against the variations of the concentrations of other components or parameters. In this article, we investigate the selectivity of microwave planar resonant sensors when measuring multicomponent solutions. Three sensors are involved, two of them having been designed looking for a more simplified system with a reduced size, and the third one has been specially developed to improve the sensitivity. The performance of these sensors is thoroughly assessed with a large set of measurements involving multicomponent solutions composed of pure water, NaCl, albumin at different concentrations and glucose at different concentrations. The impact of the simultaneous variations of the concentrations of glucose and albumin on the final measurements is analyzed, and the effective selectivity of the sensors is discussed. The results show a clear influence of the albumin concentration on the measurements of the glucose concentration, thereby pointing to a lack of selectivity for all sensors. This influence has been modeled, and strategies to manage this selectivity challenge are inferred.

Depending on their use, electrodes must have a certain size and design so as not to compromise their electrical characteristics. It is fundamental to be aware of all dependences on external factors that vary the electrochemical characteristics of the electrodes. When using implantable electrodes, the maximum charge injection capacity (CIC) is the total amount of charge that can be injected into the tissue in a reversible way. It is fundamental to know the relations between the characteristics of the microelectrode itself and its maximum CIC in order to develop microelectrodes that will be used in biomedical applications. CIC is a very complex measure that depends on many factors: material, size (geometric and effectiveness area), and shape of the implantable microelectrode and long-term behavior, composition, and temperature of the electrolyte. In this paper, our previously proposed measurement setup and automated calculation method are used to characterize a graphene microelectrode and to measure the behavior of a set of microelectrodes that have been developed in the Fraunhofer Institute for Biomedical Engineering (IBMT) labs. We provide an electrochemical evaluation of CIC for these microelectrodes by examining the role of the following variables: pulse width of the stimulation signal, electrode geometry and size, roughness factor, solution, and long-term behavior. We hope the results presented in this paper will be useful for future studies and for the manufacture of advanced implantable microelectrodes.