No abstract available.
Hormone Replacement Therapy* ; Prescriptions*

No abstract available.

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.

No abstract available.
Uterus*

No abstract available.
Accidents, Traffic* ; Aggression*

No abstract available.
Pulmonary Aspergillosis*

Child ; Child, Preschool ; Cleft Lip ; Cleft Palate ; Congenital Abnormalities ; Consensus ; Humans ; Infant ; Leukocyte Count ; Male ; Palate ; Surgery, Oral

BACKGROUND: Percutaneous Mitral Valvuloplasty (PMV) is the first-line treatment modality in selected patients with symptomatic mitral stenosis and more recently available Inoue single-balloon catheter technique produces good results with low incidence of complications. The purpose of this study was to evaluate the immediate and over 6 months follow-up results after successful PMV with an Inoue balloon and to identify the predictive factors for the results. METHODS: From May 1995 to Feburary 1997, a PMV with an Inoue balloon was tech-nically successful in 114 (95%) of the 119 patients treated at the Sejong General Hostpital. In this study, a series of echocardiographic follow-up were performed in 54 patients with rheumatic mitral stenosis, at least 6 months after their successful PMV. In PMV, the inflation was conducted in steps, starting with a recommended maximum size of balloon by the Inoue criteria. After each inflation, the mitral valve opening and competence were evaluated by Transesophageal echocardiography (TEE) and continuing increase balloon size. RESULTS: Echocardiographic follow-up assessment was performed in 54 patients serially in a interval of 3 months or 6 months. Their mean age was 46+/-11 years (24 to 66 years) and the mean total echocardiographic score was 7.1+/-1.6. A optimal result was obtained in 95% of the cases (51/54). The post-PMV mitral valve area increased to 1.95+/-0.37 cm 2 and 1.79+/-0.28 cm 2 by 2-D and Doppler method, the average transmitral mean diastolic pressure gradient decreased to 5.16+/-2.8 mmHg and LA pressure was decreased to 11.28+/-8.2 mmHg. The newly developed and aggravated mitral regurgitation was observed in 17 patients (31.5%). The restenosis was noted in 2 cases (3.7%) after 1 year follow-up. The pre-procedural echocardiographic score for leaflet mobility, thickening and calcification was more higher in patients with restenosis. There was significant tendency of decrement in the mitral valve area in patients with a echocardiographic score=8 compared with those< or =8 over 6 months after the PMV. CONCLUSION: PMV with the Inoue balloon under TEE guide as a combined treatment modality of patient with symptomatic mitral stenosis is relatively safe and achieves good immediate and midterm follow-up results. The echocardiographic score is considered as useful predictor of midterm results and restenosis after PMV with Inoue balloon.
Blood Pressure ; Catheters ; Echocardiography ; Echocardiography, Transesophageal ; Follow-Up Studies* ; Humans ; Incidence ; Inflation, Economic ; Mental Competency ; Mitral Valve ; Mitral Valve Insufficiency ; Mitral Valve Stenosis