Background: Orthognathic surgery addresses facial aesthetics and function in patients with dentofacial deformities. It is associated with changes in upper airway volume (UAV). If changes in UAV are perceived by asymptomatic patients is unclear.
Purpose: The purpose was to measure associations between changes in UAV and patient-reported benefits using patient-reported outcome measures.
Study Design: A sample presenting dentofacial deformities without reported breathing problems undergoing orthognathic surgery was retrospectively studied. Patients aged 18-30 years with 12-month follow-up were included. Patients with systemic disease, drug abuse, mental health disorder, or temporomandibular joint dysfunction were excluded.
Predictor: The predictor variable was changes in UAV measured in 3-dimensional computed tomography. Subjects were grouped into increased or decreased UAV.
Main Outcome Variable: The primary outcome variable was changes in health-related quality of life measured with Oral Health Impact Profile 49 (OHIP-49).
Covariates: Weight, height, age, sex, and sub-scaled OHIP-49 were registered. Cephalometric measurements of hard tissue movements were recorded.
Analyses: Mean, standard deviation, and a level of statistical significance at P < .05 were used. Differences in OHIP-49 were compared using unpaired t-test. The correlation between covariates and outcomes was analyzed using the Spearman's rank test. Analysis of covariance between the predictor and outcome, adjusted for covariates (body mass index), was performed.
Results: Fifty-four subjects with a mean age of 20.89 years and 52% males were enrolled. The mean change in UAV was 0.12 cm (standard deviation [SD] 9.21, P = .93) with a mean absolute deviation of 7.28 cm (SD 5.54). The mean change in OHIP-49 score was 20.93 (SD 28.90). Twenty-seven (50%) subjects had increased UAV (7.4 cm, SD 6.13) and the other had decreased (-7.17 cm, SD 5.01) (P = .01). At follow-up, equal levels of mean OHIP-49 score were found, but because of a baseline difference (15.74, P = .048), the subjects with and without increased UAV improved in OHIP-49 score 13.04 (SD 30.53) and 28.81 (SD 25.33), respectively (P = .04).
Conclusions: Because equal levels of OHIP-49 score at follow-up, changes in UAV could not be associated with patient-reported health-related quality of life. Patient-reported outcome measure evaluations of orthognathic surgical treatment for airway obstruction should be performed in patients with a perceived impairment.
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http://dx.doi.org/10.1016/j.joms.2023.09.017 | DOI Listing |
Sensors (Basel)
January 2025
State Key Laboratory of Satellite Navigation System and Equipment Technology, The 54th Research Institute, China Electronics Technology Group Corporation (CETC), Shijiazhuang 050081, China.
Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols.
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January 2025
Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China.
Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.
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February 2025
Tecnológico Nacional de México/Instituto Tecnológico de Culiacán, División de Estudios de Posgrado e Investigación, Juan de Dios Batíz 310. Col. Guadalupe, 80220 Culiacán, Sinaloa, Mexico.
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato ( var. ) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera.
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January 2025
School of Mechanical and Electrical Engineering, China University of Petroleum Huadong, Qingdao, 266580, China.
Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream.
View Article and Find Full Text PDFSci Rep
January 2025
Northeast Electric Power University, Jilin City, China.
The existing UAV inspection images are faced with many challenges for insulator defect recognition. A new multi-resolution Context Cluster CenterNet++ model is proposed. First, this paper proposes the Context Cluster method to solve the problem of low recognition accuracy caused by non-uniform distribution of targets.
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