No abstract available.
Tachycardia, Supraventricular*

Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution for these challenges by enhancing the efficiency and accuracy of breast MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced scan times while preserving image quality. This method outperforms traditional parallel imaging and compressed sensing. AI has also shown great promise for lesion classification and segmentation, with convolutional neural networks and U-Net architectures improving the differentiation between benign and malignant lesions. AI-based segmentation methods enable accurate tumor detection and characterization, thereby aiding personalized treatment planning. An AI triaging system has demonstrated the potential to streamline workflow efficiency by identifying low-suspicion cases and reducing the workload of radiologists. Another promising application is synthetic breast MR image generation, which aims to generate contrast enhanced images from non-contrast sequences, thereby improving accessibility and patient safety. Further research is required to validate AI models across diverse populations and imaging protocols. As AI continues to evolve, it is expected to play an important role in the optimization of breast MRI.

Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution for these challenges by enhancing the efficiency and accuracy of breast MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced scan times while preserving image quality. This method outperforms traditional parallel imaging and compressed sensing. AI has also shown great promise for lesion classification and segmentation, with convolutional neural networks and U-Net architectures improving the differentiation between benign and malignant lesions. AI-based segmentation methods enable accurate tumor detection and characterization, thereby aiding personalized treatment planning. An AI triaging system has demonstrated the potential to streamline workflow efficiency by identifying low-suspicion cases and reducing the workload of radiologists. Another promising application is synthetic breast MR image generation, which aims to generate contrast enhanced images from non-contrast sequences, thereby improving accessibility and patient safety. Further research is required to validate AI models across diverse populations and imaging protocols. As AI continues to evolve, it is expected to play an important role in the optimization of breast MRI.

Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution for these challenges by enhancing the efficiency and accuracy of breast MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced scan times while preserving image quality. This method outperforms traditional parallel imaging and compressed sensing. AI has also shown great promise for lesion classification and segmentation, with convolutional neural networks and U-Net architectures improving the differentiation between benign and malignant lesions. AI-based segmentation methods enable accurate tumor detection and characterization, thereby aiding personalized treatment planning. An AI triaging system has demonstrated the potential to streamline workflow efficiency by identifying low-suspicion cases and reducing the workload of radiologists. Another promising application is synthetic breast MR image generation, which aims to generate contrast enhanced images from non-contrast sequences, thereby improving accessibility and patient safety. Further research is required to validate AI models across diverse populations and imaging protocols. As AI continues to evolve, it is expected to play an important role in the optimization of breast MRI.

The mandibular contour determines the shape of the lower part of face and thus influences the appearance of head and neck. A prominent mandibular angle produce characteristic quadriangular, coarse and muscular appearance. To correct this facial contour to oval shape, the prominent angle can be contoured. the cutaneous approach has been used, but it leaves noticeble scar and has risk of facial nerve injury. Thus the intraoral approach has been used commonly. But because of restricition of operation field, the intraoral approach disadvantages of long operation time, high risk of complications. So, we have performed mandibular contouring procedure by new approach which uses reciprocating saw through stab incision under the mandibular body to correct the 9 patients of mandibular angle protrusion who visited our clinics recently. And we found that mandibular angle contouring by this new approach is easy, and makes the operation and recovery time shorter. There were no complications and cosmetic results were quite satisfactory.
Cicatrix ; Facial Nerve Injuries ; Head ; Humans ; Neck

No abstract available.

PURPOSE: The purpose of this study is to explore the experience of university students who witnessed of school violence during their formative years. METHODS: The participants in this study included 30 university students who had witnessed school violence. Analysis of the data was performed using content analysis according to Downe-Wamboldt. Data were collected from September 2015 to February 2016. RESULTS: Based on this study, there were four domains of school violence including witnessing, type of reaction, aftermath and future measures. According to the analysis, these four domains were classified into 13 variables, 32 categories and 73 significant statements. CONCLUSION: The results of this study suggest that further research is needed to develop measures to address problems that have developed for students who have witnessed school violence.
Humans ; Violence*

The purpose of the present study lied in examining the incidence, treatment and failure causes of peri-implantitis by analyzing medical charts of those patients who underwent implant placement for the past 2 years. The subjects included those patients who underwent implant placement at the present hospital from January 2001 to December 2002. 3i implants were used for the analysis for the comparison of significance. A total of 301 patients were examined, among whom 102 were females and 199, males. Implants were placed in a total of 578 cases. The number of peri-implantitis was present in a total of 29 cases (21 males and 8 females), giving the incidence at 9.6%. The evidence of peri-implantitis was seen in 60 cases, which was in 10.4% of the patients. Among those cases with peri-implantitis, 28 cases (47%) underwent bone graft and 22 cases (43%) underwent maxillary sinus lift. Furthermore, 4 of these patients had systemic diseases such as diabetes or hypertension. Regular management is important for the preven ion of peri-implantitis. In other words, early prevention through regular follow-ups to check the status of surrounding soft tissue would be needed to maintain implants.
Female ; Humans ; Hypertension ; Incidence ; Male ; Maxillary Sinus ; Peri-Implantitis* ; Retrospective Studies* ; Transplants

Anacardium ; Capsaicin ; Carcinogens ; Child ; Chronic Disease ; Cicatrix ; Esophagus ; Fibrosis ; Follow-Up Studies ; Genetic Predisposition to Disease ; Humans ; Hypersensitivity ; Immunologic Factors ; Iron ; Mouth Mucosa ; Mouth ; Mucous Membrane ; Pharynx ; Precancerous Conditions ; Skin ; Thigh ; Vitamin B Complex

PURPOSE: This study was to explore the predictors of subjective well-being of Korean older adults. METHOD: Data were collected through the structured interview using questionnaire with 199 older adults over 60 years. To identify the most effective predictor of the SWB, a stepwise regression analysis was conducted on each subscale of SWB measure. RESULTS: The results showed that receiving support from family was the effective factor of life satisfaction. Receiving support from family, health, optimism, economy, positive reappraisal, and giving support to family were the effective factors of emotional well-being. CONCLUSION: This study found that family support was the most important predictor on SWB among Korean older adults.
Adult* ; Humans ; Surveys and Questionnaires