Application of a Perception Neuron® System in Simulation-Based Surgical Training.

While multiple studies show that simulation methods help in educating surgical trainees, few studies have focused on developing systems that help trainees to adopt the most effective body motions. This is the first study to use a Perception Neuron® system to evaluate the relationship between body motions and simulation scores. Ten medical students participated in this study. All completed two standard tasks with da Vinci Skills Simulator (dVSS) and five standard tasks with thyroidectomy training model. This was repeated. Thyroidectomy training was conducted while participants wore a perception neuron. Motion capture (MC) score that indicated how long the tasks took to complete and each participant's economy-of-motion that was used was calculated. Correlations between the three scores were assessed by Pearson's correlation analyses. The 20 trials were categorized as low, moderate, and high overall-proficiency by summing the training model, dVSS, and MC scores. The difference between the low and high overall-proficiency trials in terms of economy-of-motion of the left or right hand was assessed by two-tailed t-test. Relative to cycle 1, the training model, dVSS, and MC scores all increased significantly in cycle 2. Three scores correlated significantly with each other. Six, eight, and six trials were classified as low, moderate, and high overall-proficiency, respectively. Low- and high-scoring trials differed significantly in terms of right (dominant) hand economy-of-motion (675.2 mm and 369.4 mm, respectively) (p = 0.043). Perception Neuron® system can be applied to simulation-based training of surgical trainees. The motion analysis score is related to the traditional scoring system.

Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City.

BACKGROUND: Monitoring and detecting sudden outbreaks of respiratory infectious disease is important. Emergency Department (ED)-based syndromic surveillance systems have been introduced for early detection of infectious outbreaks. The aim of this study was to develop and validate a forecasting model of respiratory infectious disease outbreaks based on a nationwide ED syndromic surveillance using daily number of emergency department visits with fever. METHODS: We measured the number of daily ED visits with body temperature ≥ 38.0 °C and daily number of patients diagnosed as respiratory illness by the ICD-10 codes from the National Emergency Department Information System (NEDIS) database of Seoul, Korea. We developed a forecast model according to the Autoregressive Integrated Moving Average (ARIMA) method using the NEDIS data from 2013 to 2014 and validated it using the data from 2015. We defined alarming criteria for extreme numbers of ED febrile visits that exceed the forecasted number. Finally, the predictive performance of the alarm generated by the forecast model was estimated. RESULTS: From 2013 to 2015, data of 4,080,766 ED visits were collected. 303,469 (7.4%) were ED visits with fever, and 388,943 patients (9.5%) were diagnosed with respiratory infectious disease. The ARIMA (7.0.7) model was the most suitable model for predicting febrile ED visits the next day. The number of patients with respiratory infectious disease spiked concurrently with the alarms generated by the forecast model. CONCLUSIONS: A forecast model using syndromic surveillance based on the number of ED visits was feasible for early detection of ED respiratory infectious disease outbreak.

Head-mounted interface for intuitive vision control and continuous surgical operation in a surgical robot system.

Although robot-assisted surgeries offer various advantages, the discontinuous surgical operation flow resulting from switching the control between the patient-side manipulators and the endoscopic robot arm can be improved to enhance the efficiency further. Therefore, in this study, a head-mounted master interface (HMI) that can be implemented to an existing surgical robot system and allows continuous surgical operation flow using the head motion is proposed. The proposed system includes an HMI, a four degrees of freedom endoscope control system, a simple three-dimensional endoscope, and a da Vinci Research Kit. Eight volunteers performed seven head movements and their data from HMI was collected to perform support vector machine (SVM) classification. Further, ten-fold cross-validation was performed to optimize its parameters. Using the ten-fold cross-validation result, the SVM classifier with the Gaussian kernel (σ = 0.85) was chosen, which had an accuracy of 92.28%. An endoscopic control algorithm was developed using the SVM classification result. A peg transfer task was conducted to check the time-related effect of HMI's usability on the system, and the paired t test result showed that the task completion time was reduced. Further, the time delay of the system was measured to be 0.72 s. Graphical abstract A head-mounted master interface (HMI), which can be implemented to an existing surgical robot system, was developed to allow simultaneous surgical operation flow. The surgeon's head motion is detected through the proposed HMI and classified using a support vector machine to manipulate the endoscopic robotic arm. A classification accuracy of 92.28% was achieved.

3D bioprinting and its in vivo applications.

The purpose of 3D bioprinting technology is to design and create functional 3D tissues or organs in situ for in vivo applications. 3D cell-printing, or additive biomanufacturing, allows the selection of biomaterials and cells (bioink), and the fabrication of cell-laden structures in high resolution. 3D cell-printed structures have also been used for applications such as research models, drug delivery and discovery, and toxicology. Recently, numerous attempts have been made to fabricate tissues and organs by using various 3D printing techniques. However, challenges such as vascularization are yet to be solved. This article reviews the most commonly used 3D cell-printing techniques with their advantages and drawbacks. Furthermore, up-to-date achievements of 3D bioprinting in in vivo applications are introduced, and prospects for the future of 3D cell-printing technology are discussed. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 444-459, 2018.

Development of stereo endoscope system with its innovative master interface for continuous surgical operation.

BACKGROUND: Although robotic laparoscopic surgery has various benefits when compared with conventional open surgery and minimally invasive surgery, it also has issues to overcome and one of the issues is the discontinuous surgical flow that occurs whenever control is swapped between the endoscope system and the operating robot arm system. This can lead to problems such as collision between surgical instruments, injury to patients, and increased operation time. To achieve continuous surgical operation, a wireless controllable stereo endoscope system is proposed which enables the simultaneous control of the operating robot arm system and the endoscope system. METHODS: The proposed system consists of two improved novel master interfaces (iNMIs), a four-degrees of freedom (4-DOFs) endoscope control system (ECS), and a simple three-dimensional (3D) endoscope. In order to simultaneously control the proposed system and patient side manipulators of da Vinci research kit (dVRK), the iNMIs are installed to the master tool manipulators of dVRK system. The 4-DOFs ECS consists of four servo motors and employs a two-parallel link structure to provide translational and fulcrum point motion to the simple 3D endoscope. The images acquired by the endoscope undergo stereo calibration and rectification to provide a clear 3D vision to the surgeon as available in clinically used da Vinci surgical robot systems. Tests designed to verify the accuracy, data transfer time, and power consumption of the iNMIs were performed. The workspace was calculated to estimate clinical applicability and a modified peg transfer task was conducted with three novice volunteers. RESULTS: The iNMIs operated for 317 min and moved in accordance with the surgeon's desire with a mean latency of 5 ms. The workspace was calculated to be 20378.3 cm3, which exceeds the reference workspace of 549.5 cm3. The novice volunteers were able to successfully execute the modified peg transfer task designed to evaluate the proposed system's overall performance. CONCLUSIONS: The experimental results verify that the proposed 3D endoscope system enables continuous surgical flow. The workspace is suitable for the performance of numerous types of surgeries. Therefore, the proposed system is expected to provide much higher safety and efficacy for current surgical robot systems.