Purpose: To propose improvements for promoting eyeball donation and managing donated corneas, this study analyzed the current system in Korea and reviewed relevant Korean laws, international standards, and foreign practices. Methods: To understand the current situation in Korea, annual reports published by the Korean Network for Organ Sharing and existing Korean laws were examined. For the international context, references were made to the National Organ Transplant Act, 21 CFR Part 1271 (Code of Federal Regulations Title 21 Part 1271), Current Good Tissue Practice guidelines of the Food and Drug Administration, and the Medical Standards of the Eye Bank Association of America in the United States. Opinions on promoting eyeball donation, improving the monitoring system for donated corneas and revising laws related to cornea management were gathered. The perspectives of 31 experts affiliated with the Korea Cornea Society were collected through a survey. Results: Currently, there are no laws or regulations that can be appropriately applied to the cornea which has properties of both organs and tissue. Additionally, there is no law regulating imported corneas. Therefore, there is a need to legislate or revise the current law; all experts who conducted the survey agreed on this. Furthermore, the current system faces limitations in the efficient procurement, stable supply, and management of donor corneas, as well as in donation promotion. To address these issues, the establishment of independent legislation for managing donated corneas and a National Central Eye Bank was proposed. This central body would oversee continuous personnel training, education, and monitoring, along with ensuring stable procurement, processing, and supply of corneas within a structured management system. Sixty-eight percent of the surveyed experts agreed with this proposal. Conclusions To establish a safe and efficient Korean corneal supply and demand system, it is imperative to enact cornea-specific laws, including the establishment of a National Central Eye Bank.

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.

Antiviral therapy is an essential treatment for chronic hepatitis B (CHB) infection. Although hypophosphatemia is an important adverse effect of antiviral agents, its clinical significance remains unclear. We investigated the incidence and clinical consequences of hypophosphatemia in a large cohort of CHB patients. Methods: This retrospective cohort study included CHB patients who started antiviral therapy between 2005 and 2015 and continued it for at least 1 year. Patients with decompensated liver cirrhosis, diabetes mellitus, hypertension, concomitant diuretic administration, and end-stage renal disease were excluded. The primary outcome was a change in renal function. Secondary outcomes included the incidence of infection and changes in serum potassium, uric acid, and total carbon dioxide (tCO2). Results: Among the 4,335 patients, hypophosphatemia developed in 75 (1.7%). During the median 2-year follow-up period, patients with hypophosphatemia showed a lower estimated glomerular filtration rate than those in the control group. The incidence of infection and changes in serum potassium, uric acid, and tCO2 were similar between groups. Conclusion: Hypophosphatemia was associated with a renal function decline in patients with CHB receiving antiviral therapy.

Background: s/Aims: Reported incidence of extrahepatic bile duct cancer is higher in Asians than in Western populations. Korea, in particular, is one of the countries with the highest incidence rates of extrahepatic bile duct cancer in the world. Although research and innovative therapeutic modalities for extrahepatic bile duct cancer are emerging, clinical guidelines are currently unavailable in Korea. The Korean Society of Hepato-Biliary-Pancreatic Surgery in collaboration with related societies (Korean Pancreatic and Biliary Surgery Society, Korean Society of Abdominal Radiology, Korean Society of Medical Oncology, Korean Society of Radiation Oncology, Korean Society of Pathologists, and Korean Society of Nuclear Medicine) decided to establish clinical guideline for extrahepatic bile duct cancer in June 2021. Methods: Contents of the guidelines were developed through subgroup meetings for each key question and a preliminary draft was finalized through a Clinical Guidelines Committee workshop. Results: In November 2021, the finalized draft was presented for public scrutiny during a formal hearing. Conclusions The extrahepatic guideline committee believed that this guideline could be helpful in the treatment of patients.

Purpose: Despite the recent success of Bruton’s tyrosine kinase (BTK) inhibitors for the treatment of Waldenstrom macroglobulinemia (WM), their indefinite treatment duration ultimately tantamount to substantial financial and emotional burden. On the other hand, fixed duration of proteasome inhibitors (PI) have shown rapid and reasonable response in WM treatment. Despite the well-known synergism between PI and immunomodulatory drugs (IMiD), there is no trials evaluating such combination in WM. Materials and Methods: Based on above, we designed this phase II study to investigate the efficacy and safety of 6 cycles of 28-day bortezomib-thalidomide-dexamethasone (VTD) regimen for treatment-naïve WM. Results: A total of 15 patients were enrolled: major response rate was 64.3%, and overall response rate was 78.6%. During the median follow-up of 41 months, median progression-free survival (PFS) was 13 months and overall survival 40 months. For responders, median duration of response was 13 months and median PFS 19 months. The most common adverse event (AE) of any grade was constipation (57.1%). The most common grade ≥ 3 AE was anemia (21.4%). Conclusion All in all, we hereby provide proof-of-concept that PI + IMiD may be an attractive backbone for fixed duration treatment. It should be noted that granting the same level of access to newer drugs globally is virtually impossible. Thus efforts to develop regimens using readily available drugs to yield similar or adequate treatment outcomes should not be disregarded. In this sense, we believe our study holds its place for its novelty and eloquently addresses achieving the daunting societal quest of health equity.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the clinical management of valvular heart diseases with reference to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee tried to reflect the recently published results on the topic of valvular heart diseases and Korean data by a systematic literature search based on validity and relevance. In part I of this article, we will review and discuss the current position of aortic valve disease in Korea.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guide‑ lines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to pre‑ sent recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.