RESUMEN
Background. This article aims to present an innovative design of a steerable surgical instrument for conventional and single-site minimally invasive surgery (MIS), which improves the dexterity and maneuverability of the surgeon while offering a solution to the limitations of current tools. Methods. The steerable MIS instrument consists of a deflection structure with a curved sliding joints design that articulates the distal tip in two additional degrees of freedom (DoFs), relative to the instrument shaft, using transmission by cables. A passive ball-joint mechanism articulates the handle relative to the instrument shaft, improves wrist posture, and prevents collision of instrument handles during single-site MIS procedures. The two additional DoFs of the articulating tip are activated by a thumb-controlled device, using a joystick design mounted on the handle. This steerable MIS instrument was developed by additive manufacturing in a 3D printer using PLA polymer. Results. Prototype testing showed a maximum tip deflection of 60° in the left and right directions, with a total deflection of 120°. With the passive ball-joint fully offset, the steerable tip achieved a deflection of 90° for the right and 40° for the left direction, with a total deflection of 130°. Furthermore, the passive ball-joint mechanism in the handle obtained a maximum range of motion of 60°. Conclusions. This steerable MIS instrument concept offers an alternative to enhance the application fields of conventional and single-site MIS, increasing manual dexterity of the surgeon and the ability to reach narrow anatomies from other directions.
Asunto(s)
Procedimientos Quirúrgicos Mínimamente Invasivos , Instrumentos Quirúrgicos , Diseño de Equipo , Rango del Movimiento ArticularRESUMEN
BACKGROUND: A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language. METHODS: Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting. Computer vision libraries were used to obtain the number of transferred points and simulated pattern cutting trace by means of tracking of the laparoscopic instrument. An artificial neural network (ANN) was trained to learn from experts and nonexperts' behavior for pattern cutting task, whereas the assessment of transferring task was performed using a preestablished threshold. Four expert surgeons in laparoscopic surgery, from hospital "Raymundo Abarca Alarcón," constituted the experienced class for the ANN. Sixteen trainees (10 medical students and 6 residents) without laparoscopic surgical skills and limited experience in minimal invasive techniques from School of Medicine at Universidad Autónoma de Guerrero constituted the nonexperienced class. Data from participants performing 5 daily repetitions for each task during 5 days were used to build the ANN. RESULTS: The participants tend to improve their learning curve and dexterity with this laparoscopic training system. The classifier shows mean accuracy and receiver operating characteristic curve of 90.98% and 0.93, respectively. Moreover, the ANN was able to evaluate the psychomotor skills of users into 2 classes: experienced or nonexperienced. CONCLUSION: We constructed and evaluated an affordable laparoscopic trainer system using computer vision, augmented reality, and an artificial intelligence algorithm. The proposed trainer has the potential to increase the self-confidence of trainees and to be applied to programs with limited resources.