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.

A 58-year-old woman visited the hospital complaining of fatigue and indigestion lasting for more than 3 months. She had no medical history other than taking a calcium plus vitamin D supplement for osteopenia. The initial blood test showed a high calcium level of 14.0 mg/dL. Additional tests were performed to differentially diagnose hypercalcemia. The blood test results were as follows: serum parathyroid hormone (PTH)=247.0 pg/mL, PTH-related peptide <1.0 pg/mL, phosphorous=2.6 mg/dL, 25-hydroxy-vitamin D=14.5 pg/mL, creatinine=1.09 mg/dL, and 24 hr urine calcium=215 mg/dL. A 4.5 cm sized cystic lesion on the intra-thyroidal space was confirmed on neck sonography and 4-dimensional parathyroid computed tomography, but technetium-99m methoxyisobutylisonitrile parathyroid scintigraphy showed equivocal results. After removal of the cystic lesion, serum calcium and PTH were normalized, and parathyroid lipoadenoma was confirmed in the postoperative pathology. Clinical features of parathyroid lipoadenoma are known to be similar to common parathyroid adenoma, but imaging studies often report negative findings. Therefore, it is necessary to better understand this rare disease for the differential diagnosis. For the final diagnosis and treatment of this disease, parathyroidectomy with intraoperative PTH measurement may be required.

Tixagevimab/cilgavimab is a monoclonal antibody used to prevent coronavirus disease 2019 among immunocompromised hosts and maintained neutralizing activity against early omicron variants. Omicron BN.1 became a dominant circulating strain in Korea early 2023, but its susceptibility to tixagevimab/cilgavimab is unclear. We conducted plaque reduction neutralization test (PRNT) against BN.1 in a prospective cohort (14 patients and 30 specimens). BN.1 PRNT was conducted for one- and three-months after tixagevimab/ cilgavimab administration and the average PRNT ND 50 of each point was lower than the positive cut-off value of 20 (12.9 ± 4.5 and 13.2 ± 4.2, respectively, P = 0.825). In the paired analyses, tixagevimab/cilgavimab-administered sera could not actively neutralize BN.1 (PRNT ND 50 11.5 ± 2.9, P = 0.001), compared with the reserved activity against BA.5 (ND 50 310.5 ± 180.4). Unlike virus-like particle assay, tixagevimab/cilgavimab was not active against BN.1 in neutralizing assay, and would not be effective in the present predominance of BA.2.75 sublineages.

Kleefstra syndrome is a rare genetic disease characterized by mental retardation, hypotonia, and a characteristic facial appearance. Furthermore, in some cases, Kleefstra syndrome is associated with various anorectal and genitourinary complications, including imperforated anus, vesicoureteral reflux, hydronephrosis, and chronic kidney disease. Herein, we present a case of Kleefstra syndrome with recurrent urinary tract infections associated with vesicoureteral reflux and rectourethral fistula, which was treated by a multidisciplinary approach.

A clinical practice guideline for patients in the dying process in general wards and their families, developed through an evidence-based process, is presented herein. The purpose of this guideline is to enable a peaceful death based on an understanding of suitable management of patients’ physical and mental symptoms, psychological support, appropriate deci-sion-making, family care, and clearly-defined team roles. Although there are limits to the available evidence regarding medical issues in patients facing death, the final recommendations were determined from expert advice and feedback, considering values and preferences related to medical treatment, benefits and harms, and applicability in the real world. This guideline should be applied in a way that takes into account specific health care environments, including the resources of medical staff and differences in the available resources of each institution. This guideline can be used by all medical institutions in South Korea.

Hypopharyngeal mass is an uncommon condition in the aerodigestive tract. There were only a few cases have been published in the medical literature. We experienced a case of incidentally detected hypopharyngeal mass during endotracheal intubation. Hypopharyngeal mass was located at the right posterior pharyngeal wall. The hypopharyngeal mass was small and not obstruct the glottis, and endotracheal intubation was performed successfully. We have also briefly discussed symptoms, diagnosis, and related problems during general anesthesia of hypopharyngeal mass.

BACKGROUND: The present study aimed at identifying the difference in the risk of microalbuminuria among individuals with various obesity phenotypes in terms of metabolic health and obesity. METHODS: This cross-sectional study included 15,268 individuals and used data from the National Health and Nutrition Survey conducted from 2011 to 2014. Obesity was defined as body mass index ≥25 kg/m2. Metabolically unhealthy was defined as meeting two or more of the following criteria: systolic and diastolic blood pressure ≥130/85 mm Hg or current use of hypertensive drugs; triglyceride level ≥150 mg/dL; high-density lipoprotein level < 40/50 mg/dL (in both men and women); and fasting blood glucose level ≥100 mg/dL or current use of oral antidiabetic medications. The participants were further classified into four subgroups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). RESULTS: A significant difference was observed in the microalbuminuria ratio among the four groups. The MHNO group was considered as the reference group, and the MHO, MUNO, and MUO groups were at an increased risk for microalbuminuria by 1.42 fold (95% confidence interval [95% CI], 1.03–1.96), 2.02 fold (95% CI, 1.61–2.53), and 3.40 fold (95% CI, 2.70–4.26), respectively, after adjusting confounding factors. CONCLUSION: The MUNO group had a higher risk of developing microalbuminuria than the MHNO group. Thus, based on this result, differences were observed in the risk of developing microalbuminuria among individuals with various obesity subtypes.
Albuminuria ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Creatinine ; Cross-Sectional Studies ; Fasting ; Humans ; Lipoproteins ; Male ; Metabolic Diseases ; Nutrition Surveys* ; Obesity* ; Phenotype* ; Triglycerides