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.
Pulmonary Aspergillosis*

No abstract available.
Accidents, Traffic* ; Aggression*

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

BACKGROUND: To identify the prevalence and related factors for vitamin D deficiency in the patients who admitted to the medical intensive care unit (ICU) of a Korean tertiary care hospital. METHODS: We retrospectively analyzed the data from ICU patients requiring mechanical ventilation (MV) for a period of > 48 h to identify the prevalence and associated factors for vitamin D deficiency. Vitamin D deficiency was defined as serum 25-hydroxyvitamin D [25(OH)D] level < 20 ng/mL. RESULTS: Among 570 patients admitted to the ICU, 221 were enrolled in the study, 194 in the vitamin D deficient group and 27 in the non-deficient group. Prevalence of vitamin D deficiency in critically ill patients was 87.8%. The patient age was lower in the vitamin D deficient group compared with the non-deficient group (64.4 ± 15.4 vs. 71.0 ± 9.6 years, p = 0.049). A higher acute physiology and chronic health evaluation II (APACHE II) score (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.10-1.37) and chronic illness (OR 3.12, 95% CI 1.08-9.01) were associated with vitamin D deficiency after adjusting for age and body mass index. Clinical outcomes of duration of MV, ICU stay, and 28- and 90-day mortality rates were not significantly different between the vitamin D deficient and nondeficient groups. CONCLUSIONS: Vitamin D deficiency was common in critically ill patients, particularly among younger patients. Higher APACHE II score and chronic illness were associated with vitamin D deficiency.
APACHE ; Body Mass Index ; Calcitriol ; Chronic Disease ; Critical Care ; Critical Illness* ; Humans ; Intensive Care Units ; Mortality ; Prevalence* ; Respiration, Artificial ; Retrospective Studies ; Tertiary Healthcare ; Vitamin D Deficiency* ; Vitamin D* ; Vitamins*