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.

Background/Aims: Long-term use of acid suppressants such as proton pump inhibitors (PPIs) and histamine 2 receptor antagonist (H2RA) has been associated with the risk of osteoporotic fracture. Acid suppressants and muco-protective agents (MPAs) are often used together as anti-ulcer agents. We evaluated the association between the risk of osteoporotic fracture and the combined use of these anti-peptic agents. Methods: A population-based case-control study was conducted by analyzing the Korean National Health Insurance Data from 2014 to 2020. Patients who had been prescribed anti-peptic agents, such as PPI, H2RA, or MPA, were included. Considering the incidence of osteoporotic fractures, the case group (n = 14,704) and control group (n = 58,816) were classified by 1:4 matching based on age and sex. Results: The use of all types of anti-peptic agents was associated with an increased risk of osteoporotic fractures (PPI: hazard osteoratio [HR], 1.31; H2RA: HR, 1.44; and MPA: HR, 1.33; all p < 0.001). Compared to PPI alone, the combined use of “PPI and H2RA” (HR, 1.58; p = 0.010) as well as “PPI, H2RA, and MPA” (HR, 1.71; p = 0.001) was associated with an increased risk of osteoporotic fracture. However, compared with PPI alone, “MPA and PPI or H2RA” was not associated with an increased risk of osteoporotic fracture. Conclusions This study found that the combined use of “PPI and H2RA” was associated with a higher risk of osteoporotic fractures. In cases where deemed necessary, the physicians may initially consider prescribing the combination use of MPA.

Background/Aims: Mucoprotective agents, such as eupatilin, are often prescribed to prevent gastrointestinal (GI) bleeding in addition to an acid suppressant despite the absence of a large-scale study. We evaluated the additional effect of eupatilin on the prevention of GI bleeding in both the upper and lower GI tract in concomitant aspirin and acid suppressant users using the nationwide database of national claims data from the Korean National Health Insurance Service (NHIS). Methods: An aspirin cohort was constructed using the NHIS claims data from 2013 to 2020. Patients who manifested with hematemesis, melena, or hematochezia were considered to have GI bleeding. A Cox proportional hazards regression model was used to determine the risk factors for GI bleeding associated with the concomitant use of GI drugs and other covariates among aspirin users. Results: Overall, a total of 432,208 aspirin users were included. The concurrent use of an acid suppressant and eupatilin (hazard ratio [HR] = 0.85, p = 0.016, vs. acid suppressant only) was a statistically significant preventive factor for GI bleeding. Moreover, a more than 3-month duration (HR = 0.88, p = 0.030) of acid suppressant and eupatilin prescription (vs. acid suppressant only) was a statistically significant preventive factor for GI bleeding. Conclusions Eupatilin administration for ≥ 3 months showed additional preventive effect on GI bleeding in concomitant aspirin and acid suppressant users. Thus, cotreatment with eupatilin with a duration of 3 months or longer is recommended for reducing GI bleeding among aspirin plus acid suppressant users.

Objective: To determine the clinical impact of the baseline sagittal imbalance severity in patients with adult spinal deformity (ASD). Methods: We retrospectively reviewed patients who underwent ≥ 5-level fusion including the pelvis, for ASD with a ≥ 2-year follow-up. Using the Scoliosis Research Society-Schwab classification system, patients were classified into 3 groups according to the severity of the preoperative sagittal imbalance: mild, moderate, and severe. Postoperative clinical and radiographic results were compared among the 3 groups. Results: A total of 259 patients were finally included. There were 42, 62, and 155 patients in the mild, moderate, and severe groups, respectively. The perioperative surgical burden was greatest in the severe group. Postoperatively, this group also showed the largest pelvic incidence minus lumbar lordosis mismatch, suggesting a tendency towards undercorrection. No statistically significant differences were observed in proximal junctional kyphosis, proximal junctional failure, or rod fractures among the groups. Visual analogue scale for back pain and Scoliosis Research Society-22 scores were similar across groups. However, severe group’s last follow-up Oswestry Disability Index (ODI) scores significantly lower than those of the severe group. Conclusion Patients with severe sagittal imbalance were treated with more invasive surgical methods along with increased the perioperative surgical burden. All patients exhibited significant radiological and clinical improvements after surgery. However, regarding ODI, the severe group demonstrated slightly worse clinical outcomes than the other groups, probably due to relatively higher proportion of undercorrection. Therefore, more rigorous correction is necessary to achieve optimal sagittal alignment specifically in patients with severe baseline sagittal imbalance.

Serological tests for Helicobacter pylori needs local validation as the diagnostic accuracy may vary depending on the prevalence of H.pylori. This study examined the diagnostic performance of two ELISA, GastroPanel® (GastroPanel ELISA; Biohit Oyj) and GENE-DIA® (GENEDIA® H. pylori ELISA, Green Cross Co.) in Korean population. One thousand seventy seven patients who visited for esophagogastroduodenoscopy between 2013 and 2023 were prospectively enrolled, and serum samples from the subjects were tested using both GastroPanel® and GENEDIA® . The two tests were compared for their diagnostic accuracy in detecting atrophic gastritis (AG), intestinal metaplasia (IM), gastric adenoma (GA), and gastric cancer (GC), and the positivity rates by age and sexwere observed. There was substantial correlation (Pearson coefficient [r] = 0.512, P < 0.001) and agreement (Cohen’s Kappa coefficient [κ] = 0.723, P < 0.001) between the results obtained using GastroPanel® and GENEDIA® . The test results from the two kits did not match perfectly with a discrepancy observed in approximately 16% of cases, that 67 subjects were positive only on GENE-DIA® while 75 subjects were positive only on GastroPanel® . The area under receiver operating characteristic curve for AG, IM, GA,and GC using GastroPanel® were 0.666, 0.635, 0.540, and 0.575, while the results tested using GENEDIA® were 0.649, 0.604, 0.553, and 0.555, respectively, without significant difference between the two results. GastroPanel® and GENEDIA® showed similar performance in terms of diagnostic accuracy; but the test results did not match perfectly. A large-scale validation study in Koreansis needed.

Purpose: This study aimed to explore customer perspectives of nursing services in tertiary hospitals. Methods: The data comprised mobile Voice Of Customer (VOC) data related to “nursing” or “nurses” generated from June 25, 2019, to December 31, 2022, in a tertiary hospital. A total of 44,727 VOC data points were collected, of which 4,040 were selected for the final analysis. Text network analysis and topic modeling were conducted using NetMiner 4.5.1. Results: Topic modeling identified five topics for positive aspects and four topics for areas requiring improvement.The positive aspects were: 1) sincere nursing care; 2) rapid response from professional medical staff; 3) teamwork for delivering customer-centric services; 4) provision and coordination of system-based healthcare services; and 5) customer-focused responsiveness. The areas requiring improvement were: 1) demand for skilled nursing care tailored to customer expectations; 2) demand for enhanced communication and reduced mechanical responses; 3) demand for appropriate handling of diverse situations; and 4) demand for overall improvements to the healthcare system, including reservation systems. Conclusion These results may be used to enhance customer and patient experiences in tertiary hospitals and are necessary for utilization from a hospital management perspective.

Purpose: This study aimed to explore customer perspectives of nursing services in tertiary hospitals. Methods: The data comprised mobile Voice Of Customer (VOC) data related to “nursing” or “nurses” generated from June 25, 2019, to December 31, 2022, in a tertiary hospital. A total of 44,727 VOC data points were collected, of which 4,040 were selected for the final analysis. Text network analysis and topic modeling were conducted using NetMiner 4.5.1. Results: Topic modeling identified five topics for positive aspects and four topics for areas requiring improvement.The positive aspects were: 1) sincere nursing care; 2) rapid response from professional medical staff; 3) teamwork for delivering customer-centric services; 4) provision and coordination of system-based healthcare services; and 5) customer-focused responsiveness. The areas requiring improvement were: 1) demand for skilled nursing care tailored to customer expectations; 2) demand for enhanced communication and reduced mechanical responses; 3) demand for appropriate handling of diverse situations; and 4) demand for overall improvements to the healthcare system, including reservation systems. Conclusion These results may be used to enhance customer and patient experiences in tertiary hospitals and are necessary for utilization from a hospital management perspective.