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http://dx.doi.org/10.3399/bjgp11X567216 | DOI Listing |
BMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
View Article and Find Full Text PDFNeurol Med Chir (Tokyo)
January 2025
College of Biological Science and Technology, National Yang Ming Chiao Tung University.
Non-enhanced head computed tomography is widely used for patients presenting with head trauma or stroke, given acute intracranial hemorrhage significantly influences clinical decision-making. This study aimed to develop a deep learning algorithm, referred to as DeepCT, to detect acute intracranial hemorrhage on non-enhanced head computed tomography images and evaluate its clinical applicability. We retrospectively collected 1,815 computed tomography image sets from a single center for model training.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Haute Ecole de Gestion Genève, HES-SO, 1227 Carouge, Switzerland.
Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions.
View Article and Find Full Text PDFSci Rep
January 2025
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Accurately predicting satellite clock deviation is crucial for improving real-time location accuracy in a GPS navigation system. Therefore, to ensure high levels of real-time positioning accuracy, it is essential to address the challenge of enhancing satellite clock deviation prediction when high-precision clock data is unavailable. Given the high frequency, sensitivity, and variability of space-borne GPS satellite atomic clocks, it is important to consider the periodic variations of satellite clock bias (SCB) in addition to the inherent properties of GPS satellite clocks such as frequency deviation, frequency drift, and frequency drift rate to improve SCB prediction accuracy and gain a better understanding of its characteristics.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK.
Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood.
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