Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: The purpose of this study was to develop an approach to alleviate the discomfort caused by sandbag compression after a bone marrow examination. This research examined the effects of applying a pressure hemostasis band on bleeding, pain, and discomfort at the bone marrow examination site. Methods: This study was conducted with a nonequivalent control group non-synchronized design. For 74 patients under evaluation who underwent bone marrow examination, sandbag compression was applied to the examination site in the control group (n=37), and a pressure hemostasis band was applied to the intervention group (n=37). In both groups, absolute bed rest was performed for two hours, and bleeding, pain, and discomfort at the examination site were measured. Results: After two hours of the bone marrow examination, there was no difference in bleeding on the gauze between the two groups (F=0.59, p=.444). Bleeding occurred in three patients in the intervention group and six in the control group (χ 2 =1.14, p=.479), with no cases of hematoma detected in either group. One hour post-examination, the control group experienced significantly higher pain (F=5.45, p=.022) and discomfort (F=5.68, p=.020) than the intervention group. However, pain and discomfort levels were similar between groups after two hours. Conclusion Compared to the sandbag compression group, the band application group showed no difference in bleeding and experienced less pain and discomfort at the examination site. This confirms that the pressure hemostasis band is a suitable alternative to sandbag compression in post-examination care.

Purpose: The purpose of this study was to develop an approach to alleviate the discomfort caused by sandbag compression after a bone marrow examination. This research examined the effects of applying a pressure hemostasis band on bleeding, pain, and discomfort at the bone marrow examination site. Methods: This study was conducted with a nonequivalent control group non-synchronized design. For 74 patients under evaluation who underwent bone marrow examination, sandbag compression was applied to the examination site in the control group (n=37), and a pressure hemostasis band was applied to the intervention group (n=37). In both groups, absolute bed rest was performed for two hours, and bleeding, pain, and discomfort at the examination site were measured. Results: After two hours of the bone marrow examination, there was no difference in bleeding on the gauze between the two groups (F=0.59, p=.444). Bleeding occurred in three patients in the intervention group and six in the control group (χ 2 =1.14, p=.479), with no cases of hematoma detected in either group. One hour post-examination, the control group experienced significantly higher pain (F=5.45, p=.022) and discomfort (F=5.68, p=.020) than the intervention group. However, pain and discomfort levels were similar between groups after two hours. Conclusion Compared to the sandbag compression group, the band application group showed no difference in bleeding and experienced less pain and discomfort at the examination site. This confirms that the pressure hemostasis band is a suitable alternative to sandbag compression in post-examination care.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: The purpose of this study was to develop an approach to alleviate the discomfort caused by sandbag compression after a bone marrow examination. This research examined the effects of applying a pressure hemostasis band on bleeding, pain, and discomfort at the bone marrow examination site. Methods: This study was conducted with a nonequivalent control group non-synchronized design. For 74 patients under evaluation who underwent bone marrow examination, sandbag compression was applied to the examination site in the control group (n=37), and a pressure hemostasis band was applied to the intervention group (n=37). In both groups, absolute bed rest was performed for two hours, and bleeding, pain, and discomfort at the examination site were measured. Results: After two hours of the bone marrow examination, there was no difference in bleeding on the gauze between the two groups (F=0.59, p=.444). Bleeding occurred in three patients in the intervention group and six in the control group (χ 2 =1.14, p=.479), with no cases of hematoma detected in either group. One hour post-examination, the control group experienced significantly higher pain (F=5.45, p=.022) and discomfort (F=5.68, p=.020) than the intervention group. However, pain and discomfort levels were similar between groups after two hours. Conclusion Compared to the sandbag compression group, the band application group showed no difference in bleeding and experienced less pain and discomfort at the examination site. This confirms that the pressure hemostasis band is a suitable alternative to sandbag compression in post-examination care.

Purpose: Biliary atresia (BA) is a rare but progressive cholangiopathy and the leading cause of pediatric liver transplantation worldwide. The Korean Association of Pediatric Surgeons (KAPS) has conducted three national surveys (2001, 2011, and 2023) to assess long-term trends in the diagnosis, treatment, and outcomes of BA. This study provides a comparative analysis of the 2nd and 3rd national surveys, with reference to selected findings from the 1st survey. Methods: This study included 453 patients from the 3rd national survey (2011–2021) and 435 patients from the 2nd survey (2001–2010), all of whom underwent Kasai portoenterostomy. Data were collected via electronic case report forms from pediatric surgical centers nationwide. Comparisons were made regarding demographics, clinical features, diagnostic patterns, operative details, follow-up outcomes, and survival. Kaplan–Meier analysis was used to evaluate long-term survival. Results: The mean number of BA patients per year remained stable between surveys (43.5 in the 2nd, 41.18 in the 3rd), though centralization of care increased, with 61.5% of cases managed by two major institutions in the 3rd survey. The median age at surgery decreased, and the use of preoperative imaging (especially magnetic resonance cholangiopancreatography) increased. The 10-year native liver survival rate declined from 59.8% to 53.7%, while overall 10-year survival improved slightly (92.9% to 93.2%). Postoperative complications, such as cholangitis and liver failure, persisted but were better categorized. The 3rd survey also reported improved mortality (4.9%) and reduced follow-up loss (11.5%) compared to the 2nd survey. Conclusion While overall survival after Kasai operation has remained high and even improved, native liver survival has slightly declined. The findings reflect earlier diagnosis, more consistent diagnostic imaging, and increasing centralization of care. These trends underscore the importance of long-term nationwide data collection in guiding future strategies for BA management in Korea.

Purpose: This study aims to investigate and compare the incidence, demographic characteristics, clinical manifestations, preoperative diagnostic methods, anatomical classifications, associated anomalies, operative treatments, and postoperative outcomes of patients with intestinal atresia treated by the members of the Korean Association of Pediatric Surgeons (KAPS) through three nationwide surveys. Methods: KAPS conducted 3 national surveys in 1998, 2010, and 2024 to examine the patients diagnosed with intestinal atresia. In preparation for the survey, we developed a customized case registration form to obtain data on patient sex, birth weight, gestational age, clinical manifestations, preoperative diagnostic methods, anatomical types, associated anomalies, operative treatments, and postoperative outcomes. Authorized KAPS members completed the case registration form. Results: The first, second, and third national surveys included 218, 222, and 236 individuals diagnosed with intestinal atresia, respectively. The male-to-female ratios were 1.5:1, 1.1:1, and 1.1:1, respectively. The first, second, and third national surveys revealed that 34.3%, 43.3%, and 53.4% of patients were born before 37 weeks of gestation, respectively. Additionally, 28.7%, 32.0%, and 40.7% of patients had a birth weight under 2,500 g. In the third national survey, duodenoduodenostomy was the most common procedure, performed in 70 out of 82 patients diagnosed with duodenal atresia. Resection and anastomosis were the main surgical procedures conducted in 47 out of 54 cases of jejunal atresia and 74 out of 92 cases of ileal atresia. The mortality rates in the first, second, and third national surveys were 13.8%, 3.6%, and 1.3% respectively, with the lowest rate observed in the third national survey. Conclusion These national surveys offer valuable insights into the current state of intestinal atresia, including specific surgical interventions and postoperative outcomes in South Korea. For pediatric surgeons aiming to enhance their understanding of intestinal atresia and its treatment options, these surveys could be an indispensable resource and guide.

Background: We aimed to examine the prevalence of burnout among imaging cardiologists in Korea and to identify its associated factors. Methods: An online survey of imaging cardiologists affiliated with university hospitals in Korea was conducted using SurveyMonkey ® in November 2023. The validated Korean version of the Maslach Burnout Inventory-Human Service Survey was used to assess burnout across three dimensions: emotional exhaustion, depersonalization, and lack of personal accomplishment. Data on demographics, work environment factors, and job satisfaction were collected using structured questionnaires. Results: A total of 128 imaging cardiologists (46.1% men; 76.6% aged ≤ 50 years) participated in the survey. Regarding workload, 74.2% of the respondents interpreted over 50 echocardiographic examinations daily, and 53.2% allocated > 5 of 10 working sessions per week to echocardiographic laboratory duties. Burnout levels were high, with a significant proportion of participants experiencing emotional exhaustion (28.1%), depersonalization (63.3%), and a lack of personal accomplishment (92.2%). Younger age (< 50 years) was correlated with higher emotional exhaustion risk, while more research time was protective against burnout in the depersonalization domain. Factors, such as being single, living with family, and specific job satisfaction facets, including uncontrollable workload and value mismatch, were associated with varying levels of burnout risk across different dimensions Conclusion Our study underscores the high burnout rates among Korean imaging cardiologists, attributed to factors such as the subjective environment and job satisfaction.Hence, evaluating and supporting cardiologists in terms of individual values and subjective factors are important to effectively prevent burnout..