Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

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: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

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: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

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.