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.

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 explore and assess the experiences of cancer patients and their caregivers who had been admitted to comprehensive nursing care service wards. Methods: Data were collected from 10 patients and 10 caregivers by in-depth interviews. The data were analyzed using content analysis of Downe-Wamboldt. Results: Three categories and seven subcategories were extracted. 1) Realizing institutional limitations of comprehensive nursing care service: ‘Wishing for precise operating systems based on patient severity,’ ‘Anticipating active caregiver participation in treatment process,’ ‘Requiring a countermeasure for safety accidents,’ 2) Professional nursing service which provides relief: ‘Patient-centered professional nursing service,’ ‘Inpatient service that provides relief for patients and caregivers,’ 3) Anticipating continuous use of the service: ‘Inpatient service which users are willing to reuse,’ ‘Wishing for expansion and reinforcement of the service.’ Conclusion Cancer patients and their caregivers experienced institutional limitations while satisfied with professional nursing service and willing to reuse the service. To improve this situation, institutional support such as separate wards for severe patients, measures for active caregiver participation and prevention of safety accidents, and adequate staffing would be helpful for relatively severe level cancer patients and their caregivers.

Purpose: The purpose of this study was to explore and assess the experiences of cancer patients and their caregivers who had been admitted to comprehensive nursing care service wards. Methods: Data were collected from 10 patients and 10 caregivers by in-depth interviews. The data were analyzed using content analysis of Downe-Wamboldt. Results: Three categories and seven subcategories were extracted. 1) Realizing institutional limitations of comprehensive nursing care service: ‘Wishing for precise operating systems based on patient severity,’ ‘Anticipating active caregiver participation in treatment process,’ ‘Requiring a countermeasure for safety accidents,’ 2) Professional nursing service which provides relief: ‘Patient-centered professional nursing service,’ ‘Inpatient service that provides relief for patients and caregivers,’ 3) Anticipating continuous use of the service: ‘Inpatient service which users are willing to reuse,’ ‘Wishing for expansion and reinforcement of the service.’ Conclusion Cancer patients and their caregivers experienced institutional limitations while satisfied with professional nursing service and willing to reuse the service. To improve this situation, institutional support such as separate wards for severe patients, measures for active caregiver participation and prevention of safety accidents, and adequate staffing would be helpful for relatively severe level cancer patients and their caregivers.

Purpose: The purpose of this study was to explore and assess the experiences of cancer patients and their caregivers who had been admitted to comprehensive nursing care service wards. Methods: Data were collected from 10 patients and 10 caregivers by in-depth interviews. The data were analyzed using content analysis of Downe-Wamboldt. Results: Three categories and seven subcategories were extracted. 1) Realizing institutional limitations of comprehensive nursing care service: ‘Wishing for precise operating systems based on patient severity,’ ‘Anticipating active caregiver participation in treatment process,’ ‘Requiring a countermeasure for safety accidents,’ 2) Professional nursing service which provides relief: ‘Patient-centered professional nursing service,’ ‘Inpatient service that provides relief for patients and caregivers,’ 3) Anticipating continuous use of the service: ‘Inpatient service which users are willing to reuse,’ ‘Wishing for expansion and reinforcement of the service.’ Conclusion Cancer patients and their caregivers experienced institutional limitations while satisfied with professional nursing service and willing to reuse the service. To improve this situation, institutional support such as separate wards for severe patients, measures for active caregiver participation and prevention of safety accidents, and adequate staffing would be helpful for relatively severe level cancer patients and their caregivers.

Objective: The Scales for Outcomes in Parkinson’s Disease–Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson’s disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). Methods: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test–retest method with an intraclass correlation coefficient (ICC) and Cronbach’s alpha coefficient were used to assess reliability. Spearman’s rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach’s alpha coefficient was 0.797, and the ICC was 0.887. Spearman’s rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusion Our results demonstrate that the K-SCOPA-Cog has good reliability and validity.

Background: The purpose of this study was to evaluate the effect of vanishing twin (VT) on maternal serum marker concentrations and nuchal translucency (NT). Methods: This is a secondary analysis of a multicenter prospective cohort study in 12 institutions. Serum concentrations of pregnancy-associated plasma protein-A in the first trimester and alpha-fetoprotein (AFP), total human chorionic gonadotrophin, unconjugated estriol, and inhibin A in the second trimester were measured, and NT was measured between 10 and 14 weeks of gestation. Results: Among 6,793 pregnant women, 5,381 women were measured for serum markers in the first or second trimester, including 65 cases in the VT group and 5,316 cases in the normal singleton group. The cases in the VT group had a higher median multiple of the median value of AFP and inhibin A than the normal singleton group. The values of other serum markers and NT were not different between the two groups. After the permutation test with adjustment,AFP and inhibin A remained significant differences. The frequency of abnormally increased AFP was also higher in the VT group than in the normal singleton group. Conclusion VT can be considered as an adjustment factor for risk assessment in the secondtrimester serum screening test.

Acute-on-chronic liver failure (ACLF) is a life-threatening disease that requires urgent liver transplantation (LT). Accurate identification of high-risk patients is essential for predicting post-LT survival. The chronic liver failure consortium ACLF score is a widely accepted risk-stratification score that includes total white blood cell (WBC) counts as a component. This study aimed to evaluate the predictive value of total and differential WBC counts for short-term mortality following LT in patients with ACLF. Methods: A total of 685 patients with ACLF who underwent LT between January 2008 and February 2019 were analyzed. Total and differential WBC counts were examined as a function of the model for end-stage liver disease for sodium (MELD-Na) score. The association between total and differential WBC counts and 90-day post-LT mortality was assessed using multivariable Cox proportional hazards regression analysis. Results: The total WBC counts and neutrophil ratio were higher in patients with ACLF than in those without ACLF. The neutrophil ratio was significantly associated with 90-day post-LT mortality after adjustment (hazard ratio [HR], 1.04; P = 0.001), whereas total WBC counts were not significantly associated with 90-day post-LT mortality in either univariate or multivariate Cox analyses. The neutrophil ratio demonstrated a relatively linear trend with an increasing MELD-Na score and HR for 90-day post-LT mortality, whereas the total WBC counts exhibited a plateaued pattern. Conclusions: Neutrophilia, rather than total WBC counts, is a better prognostic indicator for short-term post-LT mortality in patients with ACLF.