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.

BACKGROUND: The purpose of our study was to evaluate the accuracy of reduction based on postoperative computed tomography (CT) images after arthroscopic stabilization using tightrope system for unstable distal clavicle fracture. METHODS: Twelve patients with distal clavicle fracture combined with coracoclavicular (CC) ligament injury (type II, V) who received arthroscopically assisted fixation using a flip button device were evaluated for accuracy of reduction using 3-dimensional postoperative CT scan by measuring the degree of distal clavicular angulation and clavicular shortening. RESULTS: Immediate postoperative plain radiograph confirmed restoration of the CC distance (CCD) in 10 patients. At final follow-up, the CCD remained reduced anatomically on plain radiographs in these patients. All patients showed excessive posterior angulation and shortening compared to the opposite side. The average Constant score recovered to 94.8 at final follow-up. CONCLUSIONS: Indirect reduction and arthroscopic subacromial approach with flip button fixation of unstable distal clavicle fractures demonstrated favorable clinical results despite unavoidable posterior angulation of distal clavicle and shortening the total length of clavicle.
Arthroscopy ; Clavicle ; Follow-Up Studies ; Humans ; Ligaments ; Tomography, X-Ray Computed

No abstract available.
Amniotic Fluid* ; beta-Endorphin* ; Female

Pregnolone[5beta-pregnan-3alpha-ol-one(5beta3alpha)] and allopregnanolone [(5alpha-pregnan-3alpha-ol-20-one(5alpha3alpha))] are neuroactive steroids that are reduced metabolites of progesterone. It was reported that Neuroactive steroids may have anxiolytic and anticonvulsant action similar to benzodiazepines and barbiturates. Therefore, the present study was designed to assess the interaction of steroids with GABAA-benzodiazepine receptor complex. The effect of steroids on the ligands binding to GABAA receptor complex was investigated using rat cortices. 5beta3alpha and 5alpha3alpha enhanced the binding of [3H] flunitrazepam to GABAA receptor, but testosterone, progesterone and dexamethasone did not. GABA also showed the enhancement of [3H] flunitrazepam binding, but did not show the additive effect. Unlike to GABA, 5beta3alpha and 5alpha3alpha did not affect on the [3H] muscimol binding to rat cortices. From these findings, it can be concluded that Neuroactive steroids are potent positive modulators of the GABA A receptor, and do not act at GABA binding site.
Animals ; Barbiturates ; Benzodiazepines ; Binding Sites ; Dexamethasone ; Flunitrazepam ; gamma-Aminobutyric Acid ; Ligands ; Muscimol ; Pregnanolone ; Progesterone ; Rats ; Receptors, GABA-A ; Steroids* ; Testosterone

BACKGROUND: Use of venous catheter is associated with increased mortality in hemodialysis patients, but little data are available on impact of vascular access type on the discrepancy between dialysis prescription and dose. Dialysis prescription commonly exceeds the delivered dialysis dose. The purpose of this study are to evaluate the dialysis dose and efficiency between a tunneled cuffed catheters (TCCs) and arteriovenous fistulas (AVF) to the same patients on different dialysis days. METHODS: This study enrolled 35 hemodialysis patients. All patients started dialysis via tunneled cuffed catheters (Permcath(R)) in the internal jugular vein and a vascular surgeon created AVF later. We investigated prescribed spKT/V (pKt/V) and delivered spKt/V (dKt/V) within 2 months after the TCCs placement and after the AVF cannulation. Dialysis efficiencies (dKt/V/pKt/V) were compared over this two distinct times. RESULTS: Patient demographics were similar between two distinct times. The mean age of patients was 52.7 years (18 men, 17 women) and mean BMI was 21.9. 17 patients (48.6%) were diabetics. The mean pKt/V was not different (TCCs 1.63+/-0.30; AVF 1.62+/-0.29) but mean dKt/V was significantly different (TCCs 1.38+/-0.31; AVF 1.46+/-0.34) (p<0.05). The dialysis efficiencies were 85.14% for TCCs and 90.74% for AVF (p<0.05). There were no differences in sex, or diabetics. Underdialysis (delivered spKt/V <1.3) was greater in TCCs (17/35; 48.6 %), compared to AVF (10/35; 28.6%). Underdialysis was more common in larger patients. CONCLUSION: Our data suggest that AVF tend to receive more dialysis dose than tunneled cuffed catheters and underdialysis exposure are more common with catheter use.
Arteriovenous Fistula ; Catheterization ; Catheters ; Demography ; Dialysis* ; Humans ; Jugular Veins ; Male ; Mortality ; Prescriptions ; Renal Dialysis*

A cross-sectional study was conducted to assess the association between perceived occu-pational psychosocial stress at work and the work-related musculoskeletal disorders (WRMSD) among employees using video display terminals. The study included 111 female telecommunication employees from three companies at ChungBuk province. Cases of WRMSD were defined using symptom questionnaire and physical examination conducted by rehabilitation specialist. Information on demographics, individual factors, and perceived psychosocial stress, were obtained by self-administered questionnaire. For assessing perceived psychosocial stress, we used variables from 'job strain model' proposed by Karasek. Associations between perceived psychosocial stress and WRMSD were assessed by multiple logistic regression models. Forty nine (44% to the total) subjects met our operational definition for WRMSD. Age, seniority, housing load were not associated with WRMSD. Perceived psychosocial stress was associated with WRMSD [odds ratio=3.28, 95% confidence interval: 1.05-10.19]. This study suggests that perceived occupational psychosocial stress is related to the prevalence of WRMSD.
Chungcheongbuk-do ; Computer Terminals* ; Cross-Sectional Studies ; Demography ; Female ; Housing ; Humans ; Logistic Models ; Physical Examination ; Prevalence ; Questionnaires ; Rehabilitation ; Specialization ; Telecommunications

BACKGROUND AND OBJECTIVES: The patient presenting to the emergency department with the complaint of dizziness is one of the most common as well as most challenging problems confronting the emergency physician today. Dizziness is a vague symptom of disease ranging from serious to benign. The differential diagnosis and proper management of dizziness in a department of emergency medicine are the most important things. We undertook this study to evaluate the character and clinical analysis of the dizzy patients and to discuss the significance and necessity of primary care in a department of emergency medicine. MATERIALS AND METHOD: From September 1999 to October 2003, we sampled the 1,371 dizzy patients who visited the Emergency Department(ED). Of these patients, the 650 patients performed electronystagmography(ENG) and rotatory chair test were selected, except for the patients with loss of consciousness, known psychiatric disease or direct brain injury. RESULTS: The 650 patients were attributed to acute peripheral vestibulopathy in 88.5% (575 patients) of patients, benign paroxysmal positional vertigo in 9.5% (62 patients) of patients, central origin in 1.4% (9 patients) of patients, other organic origin in 0.6% (4 patients) of patients. In a retrospective analysis of patients with central origin, the symptoms estimated at central origin, for example, persistent headache, were found. CONCLUSION: The exact diagnosis and management for the dizzy patients in a department of emergency medicine require the primary physician's concern and understanding for dizziness.
Brain Injuries ; Diagnosis ; Diagnosis, Differential ; Dizziness ; Emergencies* ; Emergency Medicine* ; Emergency Service, Hospital ; Headache ; Humans ; Primary Health Care ; Retrospective Studies ; Unconsciousness ; Vertigo ; Vestibular Neuronitis

OBJECTIVE: The aim of this study is to evaluate the value of vaginal ultrasonographic cervical length in prediction of preterm delivery in patients with preterm labor. METHODS: One-hundred twenty-nine women in preterm labor at 24-34 weeks of gestation were studied retrospectively. Transvaginal ultrasonography for measurement of cervical length was performed at the time of admission. Receiver-operating characteristic (ROC) curve analysis, partial correlation coefficient, and multiple logistic regression analysis were used for statistical analysis. RESULTS: The mean gestational age at admission was 30.2 +/- 2.5 weeks and the mean gestational age at delivery was 35.7 +/- 3.9 weeks. The median admission-to-delivery interval was 864 hours (24-2616). ROC curve analysis showed a significant relationship between cervical length and preterm delivery (area under the curve=0.82, p<0.001). The diagnostic indices of cervical length (cut-off value 2.5 cm) by ROC curve were sensitivity of 86%, specificity of 64%, and positive and negative predictive value of 54% and 90%. Multiple logistic regression analysis indicated that cervical length was an independent predictor of preterm delivery after adjustment of confounding factors (x2=0.326, p<0.01). Admission-to-delivery interval in patients with cervical length >or=2.5 cm was significantly shorter than that in patients with cervical length >2.5 cm. CONCLUSION: Transvaginal ultrasonographic measurement of cervical length is a useful predictor of preterm delivery in patients with preterm labor and intact membranes.
Female ; Gestational Age ; Humans ; Logistic Models ; Membranes ; Obstetric Labor, Premature* ; Pregnancy ; Retrospective Studies ; ROC Curve ; Sensitivity and Specificity ; Ultrasonography