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 evaluate the influence of changes in mitral valve area (MVA) and left atrial pressure on pulmonary vein flow (PVF) we analyzed PVF with transesophageal echocardiography (TEE) before, after and one-year after percutaneous mitral valvuloplasty (PMV) in patients with mitral stenosis (MS) in sinus rhythm. METHODS: PMV was guided with TEE. Follow-up TEE was done about one year after PMV. MVA and transmitral mean gradient (TMG) were measured. Systolic velocity (S), diastolic velocity (D), atrial reversal velocity (AR), their time-velocity integral (S-TVI, D-TVI, AR-TVI) and their ratio (S/D ratio, S-TVI/D-TVI ratio were evaluated. RESULTS: The number of patients was twenty-two (F:20). The age was 39+/-9 years (range:26-64). Follow-up duration was 16+/-6 months (range:7-28). MVA increased from 0.9+/-0.2 cm2 to 1.9+/-0.3 cm2 after PMV and decreased to 1.7+/-0.3 cm2 on follow-up TEE significantly. TMG decreased from 15.4+/-4.3 mmHg to 5.5+/-1.9 mmHg after PMV and was 6.2+/-2.4 mmHg on follow-up. S increased significantly on follow-up at both pulmonary vein (PV). D increased on follow-up at left PV. S/D ratio increased on follow-up at both PV. AR increased on follow-up at both PV. S-TVI increased after PMV at left PV and increased on follow-up at both PV. D-TVI had no change. S-TVI/D-TVI ratio increased on follow-up at left PV. AR-TVI increased on follow-up at right PV. CONCLUSIONS: The main changes after PMV in patients with MS in sinus rhythm were increasing tendency in S, S-TVI, S/D ratio, S-TVI/D-TVI ratio and AR. And these changes were statistically significant on follow-up TEE rather than immediately after PMV.
Atrial Pressure ; Echocardiography* ; Echocardiography, Transesophageal ; Follow-Up Studies ; Humans ; Mitral Valve ; Mitral Valve Stenosis* ; Pulmonary Veins