3D printing endobronchial models for surgical training and simulation

Lung cancer is the leading cause of cancer-related deaths. Many methods and devices help acquire more accurate clinical and localization information during lung interventions and may impact the death rate for lung cancer. However, there is a learning curve for operating these tools due to the complex structure of the airway. In this study, we first discuss the creation of a lung phantom model from medical images, which is followed by a comparison of 3D printing in terms of quality and consistency. Two tests were conducted to test the performance of the developed phantom, which was designed for training simulations of the target and ablation processes in endochonchial interventions. The target test was conducted through an electromagnetic tracking catheter with navigation software. An ablation catheter with a recently developed thermochromic ablation gel conducted the ablation test. The results of two tests show that the phantom was very useful for target and ablation simulation. In addition, the thermochromic gel allowed doctors to visualize the ablation zone. Many lung interventions may benefit from custom training or accuracy with the proposed low-cost and patient-specific phantom

Origami lesion-targeting device for CT-guided interventions

The objective of this study is to preliminarily evaluate a lesion-targeting device for CT-guided interventions. The device is created by laser cutting the structure from a sheet of medical grade paperboard, 3D printing two radiocontrast agent grids onto the surface and folding the structure into a rectangular prism with a viewing window. An abdominal imaging phantom was used to evaluate the device through CT imaging and the targeting of lesions for needle insertion. The lesion-targeting trials resulted in a mean targeting error of 2.53 mm (SD 0.59 mm, n = 30). The device is rigid enough to adequately support standard biopsy needles, and it attaches to the patient, reducing the risk of tissue laceration by needles held rigidly in place by an external manipulator. Additional advantages include adequate support for the insertion of multiple surgical tools at once for procedures such as composite ablation and the potential to guide off-axial needle insertion. The low-cost and disposability of the device make it well-suited for the minimally invasive image-guided therapy environment

Introduction of electric vehicle charging stations to university campuses : A case study for the university of Georgia from 2014 to 2017

Electric vehicles (EVs) are becoming increasingly popular in the United States of America (USA). EVs attract buyers with benefits including energy efficiency and environmental friendliness. As EV usage grows, more public spaces are installing EV charging stations. This paper presents a comprehensive analysis of EV charging station usage at the University of Georgia (UGA) in Athens, Georgia. Three ChargePoint EV charging stations at UGA were used to collect data about each of 3204 charging events that occurred from 10 April 2014 to 20 June 2017. The charging event data included start date, start time, length of parking time, length of charging time, amount of energy delivered, and the postal code entered by the user during ChargePoint account registration. Analytical methods were proposed to obtain information about EV charging behavior, charging station occupancy, and geolocation of charging station users. The methodology presented here was time- and cost-effective, as well as scalable to other organizations that own charging stations. Because this study took place at a university, the results presented here can be used as a reference for EV charging station usage in other college towns in the USA that do not have EV charging stations but are planning to develop EV infrastructure

Zika virus on youtube: An analysis of english-language video content by source

Objectives The purpose of this study was to describe the source, length, number of views, and content of the most widely viewed Zika virus (ZIKV)-related YouTube videos. We hypothesized that ZIKV-related videos uploaded by different sources contained different content. Methods The 100 most viewed English ZIKV-related videos were manually coded and analyzed statistically. Results Among the 100 videos, there were 43 consumer-generated videos, 38 Internet-based news videos, 15 TV-based news videos, and 4 professional videos. Internet news sources captured over two-thirds of the total of 8 894 505 views. Compared with consumer-generated videos, Internet-based news videos were more likely to mention the impact of ZIKV on babies (odds ratio [OR], 6.25; 95% confidence interval [CI], 1.64 to 23.76), the number of cases in Latin America (OR, 5.63; 95% CI, 1.47 to 21.52); and ZIKV in Africa (OR, 2.56; 95% CI, 1.04 to 6.31). Compared with consumer-generated videos, TV-based news videos were more likely to express anxiety or fear of catching ZIKV (OR, 6.67; 95% CI, 1.36 to 32.70); to highlight fear of ZIKV among members of the public (OR, 7.45; 95% CI, 1.20 to 46.16); and to discuss avoiding pregnancy (OR, 3.88; 95% CI, 1.13 to 13.25). Conclusions Public health agencies should establish a larger presence on YouTube to reach more people with evidence-based information about ZIKV

Pedagogical demonstration of twitter data analysis: A case study of world AIDS day, 2014

As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models

Correction: Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges. [Robotics 2019, 8, 4]doi 10.3390/robotics8010004

10.3390/ROBOTICS8040093Robotics849

Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges

10.3390/robotics8010004Robotics81