RESUMEN
BACKGROUND: In this research, imaging techniques such as CT and X-ray are used to locate important muscles in the shoulders and legs. Athletes who participate in sports that require running, jumping, or throwing are more likely to get injuries such as sprains, strains, tendinitis, fractures, and dislocations. One proposed automated technique has the overarching goal of enhancing recognition. OBJECTIVE: This study aims to determine how to recognize the major muscles in the shoulder and leg utilizing X-ray CT images as its primary diagnostic tool. METHODS: Using a shape model, discovering landmarks, and generating a form model are the steps necessary to identify injuries in key shoulder and leg muscles. The method also involves identifying injuries in significant abdominal muscles. The use of adversarial deep learning, and more specifically Deep-Injury Region Identification, can improve the ability to identify damaged muscle in X-ray and CT images. RESULTS: Applying the proposed diagnostic model to 150 sets of CT images, the study results show that Jaccard similarity coefficient (JSC) rate for the procedure is 0.724, the repeatability is 0.678, and the accuracy is 94.9% respectively. CONCLUSION: The study results demonstrate feasibility of using adversarial deep learning and deep-injury region identification to automatically detect severe muscle injuries in the shoulder and leg, which can enhance the identification and diagnosis of injuries in athletes, especially for those who compete in sports that include running, jumping, and throwing.
Asunto(s)
Músculo Esquelético , Tomografía Computarizada por Rayos X , Humanos , Rayos X , Tomografía Computarizada por Rayos X/métodos , Radiografía , Músculo Esquelético/diagnóstico por imagen , AtletasRESUMEN
OBJECTIVES: Currently, the research results regarding the bilateral temporomandibular joint symmetry in patients at different ages with unilateral complete cleft lip and palate (UCLP) are still controversial. In this study, the position of condyle in the articular fossa and morphology of condyle in UCLP patients at different developmental stages was measured and analyzed to explore the asymmetry difference, which can provide a new theoretical basis for the sequential therapy. METHODS: A total of 90 patients with UCLP were divided into a mixed dentition group (31 cases), a young permanent dentition group (31 cases) and an old permanent dentition group (28 cases) according to age and dentition development. Cone beam computed tomography (CBCT) images were imported into Invivo5 software for 3D reconstruction, and the joint space, anteroposterior diameter, medio-lateral diameter, and height of condylar were measured, and its asymmetry index was calculated. RESULTS: The asymmetry index of condylar height and anteroposterior diameter among the 3 groups, from small to large, was the mixed dentition group
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Labio Leporino , Fisura del Paladar , Humanos , Labio Leporino/diagnóstico por imagen , Fisura del Paladar/diagnóstico por imagen , Articulación Temporomandibular/diagnóstico por imagen , Relevancia ClínicaRESUMEN
The aim of this study was to investigate the association between physical activity (PA), problematic smartphone use (PSU), and burnout, as well as to identify whether there is a mediating role for PSU. We recruited 823 college students (Mage = 18.55, SD = 0.83) from Wuhan, China, in December 2022, including 499 males and 324 females. Demographic information, the International Physical Activity Questionnaire-Short Form (IPAQ-SF), the Smartphone Addiction Scale-Short Version (SAS-SV), and the Maslach Burnout Inventory-Student Survey (MBI-SS) were used for assessments. Pearson correlation analysis showed that PA was significantly associated with PSU (r = -0.151, p < 0.001), PSU was significantly associated with burnout (r = 0.421, p < 0.001), and the association between PA and burnout was not statistically significant (r = -0.046, p > 0.05). The results of the mediation model test showed that PA could not predict burnout directly; it instead predicted burnout entirely indirectly through PSU. Furthermore, PSU mediated the predictive effect of PA on exhaustion and cynicism. In conclusion, there is no direct connection between PA levels and burnout. PA indirectly affects burnout through PSU, but does not fully apply to the three different dimensions of exhaustion, cynicism, and professional efficacy.