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.

This study aimed to elucidate the clinical characteristics of patients diagnosed with toxoplasmosis in Korea. We collected and analyzed the specific research data of 5,917 patients from the Health Insurance Review and Assessment (HIRA; 2007–2020) and 533 electronic medical records (EMRs; 2003–2021) of Korean patients. The HIRA data showed that toxoplasmosis is an endemic disease that occurs constantly in Korea, with a large proportion of patients complaining of ocular symptoms. Of the 533 patients for whom EMR data were available, 54.6% were diagnosed with toxoplasmosis; ocular toxoplasmosis (35.7%), congenital toxoplasmosis (4.7%), cerebral toxoplasmosis (4.1%), pulmonary toxoplasmosis (0.4%), and toxoplasma hepatitis (0.6%), in order of frequency. In ocular cases, 54.4% of the patients had diverse ocular pathologies. Toxoplasmosis in Korea is characterized by a high frequency of ocular symptoms, most patients are adults, and 51.8% of patients with seropositivity were positive for IgG, suggesting prior infection. This study highlights that patients with ocular symptoms are included in the major diagnosis group for acquired toxoplasmosis in Korea.

Purpose: In this study, we evaluated 66 patients diagnosed with adenoid cystic carcinoma (ACC) enrolled in two Korean Cancer Study Group trials to investigate the response and progression patterns in recurrent and/or metastatic ACC treated with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs). Materials and Methods: We evaluated 66 patients diagnosed with ACC who were enrolled in the Korean Cancer Study Group trials. The tumor measurements, clinical data, treatment outcomes, and progression patterns of therapy were analyzed. Results: In the 66 patients (53 receiving axitinib and 13 receiving nintedanib), the disease control rate was 61%, and three patients achieved partial response. The median follow-up, median progression-free survival (PFS), overall survival, and 6-month PFS rate were 27.6%, 12.4%, and 18.1% months and 62.1%, respectively. Among 42 patients who experienced progression, 27 (64.3%) showed target lesion progression. Bone metastasis was an independent poor prognostic factor. Conclusion Overall, most patients demonstrated stable disease with prolonged PFS; however, prominent target lesion progression occurred in some patients. Thus, PFS may capture VEGFR-TKI efficacy better than the objective response rate.

Background: An idiopathic outflow tract premature ventricular complex (OT-PVC) is a common arrhythmia, and the accuracy of site of origin prediction using the 12-lead electrocardiogram (ECG) algorithm is not high. There are no studies about a systematic strategy that can provide practical help to electrophysiologists in OT-PVC mapping and ablation. This study aims to evaluate the efficacy and safety of the proposed ablation protocol and establish an optimal catheter ablation strategy by simultaneously investigating and synthesizing various indicators observed during the mapping procedure. Methods: and designThis study (ABOUT-PVC) was designed as a prospective multicenter study to enroll 210 patients from 11 tertiary university hospitals over an estimated 27 months. Patients with idiopathic OT-PVC requiring catheter ablation will receive the procedure through a proposed ablation strategy and will be followed up for at least 12 months. The primary outcome is the acute procedural success rate. The secondary outcomes are clinical success rate, procedure time, complication rate, symptom relief, and changes in echocardiographic parameters. Conclusions The ABOUT-PVC study was designed to investigate the efficacy and safety of the proposed ablation strategy and establish an optimal catheter ablation strategy. We expect this study to overcome the limitations of the ECG prediction algorithms and provide a practical guide to electrophysiologists, increasing the procedure’s success rate and reducing complications and procedure time.