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: Balloon mitral valvuloplasty is a favorable procedure as a therapy for mitral stenosis because it minimizes morbidity and shorten hospital stay compared with surgical mitral commissurotomy or mitral valve replacement. Recent reports about concomitant transesophageal echocardiography guide in addition to fluoroscopy suggest that transesophageal echocardiograpy can provide additional benefits during balloon mitral valvuloplasty especially in transseptal puncture, balloon positioning, evaluation of immediate result, and early detection of complications. We performed this study to identify the potential benefits of on-line transesophageal echocardiography guide during balloon mitral valvuloplasty. METHOD: We performed balloon mitral valvuloplasty under on-line transesophageal echocardiography guide in addition to fluoroscopy in 70 patients(male:14, female:56, mean age:44+/-13) with rheumatic mitral stenosis from May 1995 to May 1996. Thirty-two(46%) patients had atrial fibrillation. Included patients were symptomatic with more than NYHA class 2 symptom. Patients with mitral valve score more than 11 and mitral regurgitation more than 2/4 were excluded. Inoue balloons were utilized in all cases. RESULTS: The average mitral valve area increased from 0.9+/-0.2cm2 before valvuloplasty to 1.8+/-0.4cm2 after valvuloplasty(p<0.0001). The averagetransmitral pressure gradient measured by continuous wave Doppler decreased from 14+/-6mmHg before valvuloplasty to 5+/-2mmHg after valvuloplasty(p<0.0001), and the average left atrial pressure measured by catheterization decreased form 22+/-8 mmHg before valvuloplasty to 11+/-5mmHg after valvuloplasty(p<0.0001). The average procedure time was 64+/-22 minutes(ranged from 13 to 150 minutes) and the average fluoroscopy time was 19+/-15 minutes(ranged from 1 to 94 minutes). Two patients underwent surgery due to severe mitral regurgitation associated with papillary muscle rupture which developed after valvuloplasty. In one patient, transesophageal echocardiography detected pericaridal tamponade during the procedure and the transducer was quickly switched to transthoracic transducer to guide the pericardial puncture site. The pericardial tamponade was drained with pigtail catheter and the patient underwent balloon mitral valvuloplasty successfully a week later. Four patients were pregnant at the time of the valvuloplasty procedure and the valvuloplasty was successfully performed with minimal fluoroscopy time(1-3 minutes) without complications in all four patients. Five patients had thrombus in left atrial appendage, but the transesophageal echocardiography was useful in monitoring the ballon position during the procedure and the valvuloplasty was successfully performed without embolic complications in all five patients. CONCLUSION: The transesophageal echocardiography is a very useful guiding adjunct during balloon mitral valvuloplasty in transseptal puncture, balloon positioning, evaluation of immediate result, early detection of complications, and shortening fluoroscopy time especially in pregnant women.
Atrial Appendage ; Atrial Fibrillation ; Atrial Pressure ; Cardiac Tamponade ; Catheterization ; Catheters ; Echocardiography, Transesophageal* ; Female ; Fluoroscopy ; Humans ; Length of Stay ; Mitral Valve ; Mitral Valve Insufficiency ; Mitral Valve Stenosis ; Papillary Muscles ; Pregnant Women ; Punctures ; Rupture ; Thrombosis ; Transducers