Aim: Approximately 424,000 out-of-hospital cardiac arrests (OHCA) occur in the US annually. As automated external defibrillators (AED) are an important part of the community response to OHCA, we investigated how well the spatial demand (likelihood of OHCA) was met by the spatial supply (AEDs) in a dense urban environment.
Methods: Using geographic information system (GIS) software, we applied kernel density and optimized hot spot procedures with two differently-sized radii to model OHCA incidence rates from existing studies, providing an estimate of OHCA likelihood at a given location. We compared these density maps to existing AED coverage in the study area. Descriptive statistics summarized coverage by land use.
Results: With a 420-ft buffer, we found that 56.0% (79.9%, 840-ft buffer) of the land area in the city center was covered by existing AEDs at, though 70.1 (91.5)% of the OHCA risk was covered using kernel density and 79.8% (98.1) was covered using hot spot analysis.
Conclusions: The difference in coverage by area and risk seems to indicate efficient placement of existing AEDs. Our findings also highlight the possible benefits to expanding the influence of AEDs by lowering search times, and identify opportunities to improve AED coverage in the study area. This article offers one method by which local officials can use spatial data to prioritize attention for AED placement and coverage.
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http://dx.doi.org/10.1016/j.resuscitation.2016.09.021 | DOI Listing |
Int J Med Robot
February 2025
Faculty of Health, Education, Medicine and Social Care, Medical Technology Research Centre, The Institute of Excellence in Robotic Surgery, Anglia Ruskin University, Chelmsford, UK.
Background: The human eye consists of highly sensitive, hydrated, and relatively thin tissues, making precise control and accurate force estimation crucial in robotic eye surgery. This paper introduces a novel control method and state observer designed for a gripper surgical instrument used on the external ocular surface during robotic eye surgery.
Methods: A novel state observer, operating in tandem with the controller, estimates the applied force.
J Robot Surg
January 2025
Sengupta Urology, Glen Waverley, Vic, Australia.
This study compares laparoscopic (LRP) and robotic-assisted (RARP) radical prostatectomy to identify external and internal disruptive events, focusing on tasks that require heightened attention and coordination among the surgical team. Observations conducted across three hospitals in Australia and China. Data collection was rigorously ensured through the analysis of video recordings and consultations with surgeons, followed by statistical analysis using the Wilcoxon Signed Rank test.
View Article and Find Full Text PDFAdv Mater
January 2025
National Key Laboratory of Microwave Photonics, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
As one of the typical applications of metamaterials, the invisibility cloak has raised vast research interests. After many years' research efforts, the invisibility cloak has extended its applicability from optics and acoustics to electrostatics and thermal diffusion. One scientific challenge that has significantly restricted the practical application of the invisibility cloak is the strong background dependence, that is, all passive cloaking devices realized thus far are unable to resist variation in the background refractive index.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Radiology, Yiyang Central Hospital, Yiyang, China.
Objectives: To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) with deep learning-based automatic segmentation, for preoperative prediction of the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.
Methods: This retrospective study collected data from 360 patients with uterine fibroids who underwent HIFU treatment. The dataset was sourced from Center A (training set: N = 240; internal test set: N = 60) and Center B (external test set: N = 60).
NPJ Digit Med
January 2025
Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, 515041, China.
Label noise is a common and important issue that would affect the model's performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.
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