No abstract available.
Deoxyglucose* ; Shock*

No abstract available.
Humans

PURPOSE: This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. METHODS: This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. RESULTS: An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. CONCLUSIONS: The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.
Acceleration ; Arm ; Humans ; Posture ; Urination* ; Vibration

Purpose: Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation. Methods: The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model’s performance was evaluated using accuracy, precision, and recall as metrics. Results: The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model. Conclusions The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.

Parovarian cyst is usually simple cyst, and accounts for 10%–20% of adnexal masses. Borderline or malignant parovarian tumor is rare, and it contains papillary projection at the smooth inner wall. We report the ultrasonography, CT and MRI findings of a parovarian serous borderline tumor in 19-year-old female presenting left lower quadrant abdominal discomfort for 2 days.

Although the efficacy of surgery followed by taxane- and platinum-based systemic chemotherapy has been clearly demonstrated in the standard first-line treatment of epithelial ovarian cancer, the role of radiation therapy for distant lymph node metastasis in patients with epithelial ovarian cancer is not well-established due to a lack of reported studies. We identified four patients who underwent selective adjuvant radiation therapy for neck and para-aortic lymph node lesions after primary debulking surgery between 2020 and 2022, followed by platinum-based chemotherapy for stage 4B high-grade serous ovarian cancer. Through a retrospective review of medical records, we analyzed patient clinicopathologic features, treatment course, and imaging findings. The median age was 49.25 years (range, 46–54 years). All patients had the International Federation of Gynecology and Obstetrics stage 4B disease. Following primary debulking surgery, all patients received weekly paclitaxel-carboplatin chemotherapy and maintenance treatment with poly(ADP-ribose) polymerase (PARP) inhibitors. All patients received selective adjuvant radiation therapy for neck and para-aortic lymph node metastasis before PARP inhibitor maintenance. The median follow-up time was 36.75 months (range, 19–45 months). All patients achieved a complete response. None of the patients experienced disease recurrence or died during the follow-up period. The management of distant lymph node metastasis in patients with epithelial ovarian cancer remains a matter of debate. Selective adjuvant radiation therapy in first-line treatment for ovarian cancer appears to be a feasible approach with maintenance therapy for stage 4B epithelial ovarian cancer.

The first author's affiliation should be corrected.

OBJECTIVES: The purpose of present study is to identify the differences between temperament and character traits among adolescents classified by ego resilience and ego control level. METHODS: A Junior Temperament and Character Inventory (TCI) was performed on participants, and a California Child Q-Test (CCQ) was tested for by the parents of the adolescent participants. RESULTS: The result of the factor analysis and discriminant analysis show that the participants are classified into three different groups based on CCQ score and item response patterns. The three groups are named the ego resilience group, the undercontrolled group, and the overcontrolled group. The ego resilience group indicates that they have a high level of interest and curiosity but yet they are not easily tempered. Moreover, they behave proactively with a reasonable level of responsibility in order to improve themselves. In contrast, among the undercontrolled group, it is observed that, although the group shows even higher level of interest, curiosity and honesty, they are careless, more impulsive, and have a lack of delayed gratification and shame. CONCLUSION: The overcontrolled group shows that they are the most cautious and reflective group among the three but also show emotional instability such as strong criticism and passive-aggressiveness. In this study, implications and limitations are discussed.
Adolescent ; California ; Child ; Ego ; Exploratory Behavior ; Humans ; Parents ; Temperament

Recent developments in virtual, augmented, and mixed reality have introduced a considerable number of new devices into the consumer market. This momentum is also affecting the medical and health care sector. Although many of the theoretical and practical foundations of virtual reality (VR) were already researched and experienced in the 1980s, the vastly improved features of displays, sensors, interactivity, and computing power currently available in devices offer a new field of applications to the medical sector and also to urology in particular. The purpose of this review article is to review the extent to which VR technology has already influenced certain aspects of medicine, the applications that are currently in use in urology, and the future development trends that could be expected.
Biofeedback, Psychology ; Foundations ; Health Care Sector ; Urology*

Purpose: This paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them. Methods: This paper proposes the optimal ureter stone detection model using various AI technologies. The use of AI technology compares and evaluates methods such as machine learning (support vector machine), deep learning (ResNet-50, Fast R-CNN), and image processing (watershed) to find a more effective method for detecting ureter stones. Results: The final value of sensitivity, which is calculated using true positive (TP) and false negative and is a measure of the probability of TP results, showed high recognition accuracy, with an average value of 0.93 for ResNet-50. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery. Conclusions The general situation in the most effective way to the detection stone can be found. But a variety of variables may be slightly different the difference through the term could tell. Future works, on urological diseases, are diverse and the research will be expanded by customizing AI models specialized for those diseases.