Background: Rapid pathogen identification and antibiotic-susceptibility tests (ASTs) are important for treating bloodstream infections. We compared the performance of the QMAC-dRAST and BD Phoenix M50 direct AST (dPhoenix) systems using bacterial pellets prepared from positive blood culture broth and evaluated their impact on treatment modification. Methods: Direct AST results for 106 Enterobacterales isolates were retrospectively reviewed. Conventional broth microdilution was used to calculate categorical agreement (CA), very major error (VME), major error (ME), and minor error (mE). For isolates showing high VMEs in both methods, supplementary tests were performed. Clinical impact was evaluated by calculating the time required to obtain AST results (time-to-result) and observing changes in antibiotics prescribed after performing ASTs. Results: Both systems showed acceptable overall CA, VME, ME, and mE values (QMACdRAST: 93.6%, 1.6%, 0.9%, and 5.3%, respectively; dPhoenix: 93.1%, 0.9%, 0.6%, and 6.2%, respectively). Piperacillin–tazobactam showed high VMEs with QMAC-dRAST (4/20, 20.0%) and dPhoenix (3/20, 15.0%). Colony AST on 13 isolates revealed that QMACdRAST testing yielded lower minimal inhibitory concentrations (MICs) for piperacillin–tazobactam with three isolates, whereas dPhoenix testing yielded higher MICs with two isolates and lower MICs with two isolates. The average time-to-result was 20.8 hr and 30.1 hr for QMAC-dRAST and dPhoenix, respectively (P < 0.001). After AST, the number of optimal treatments increased from 43 (46.7%) to 72 (78.3%) (P < 0.001). Conclusions The QMAC-dRAST and dPhoenix systems provided reliable AST results with a short time-to-result. However, we recommend performing complementary tests, such as the disk diffusion test, for piperacillin–tazobactam.

Purpose: Improvements in surgical quality and patient safety are critical components of the healthcare system. Despite excellent cancer survival rates in Korea, there is a lack of standardized postoperative complication management systems.To address this gap, the Korean Surgical Society initiated the development of the Korean Quality Improvement Platform in Surgery (K-QIPS) program. Methods: K-QIPS was successfully launched in 87 general hospitals. This nationwide surgical quality improvement program covers 5 major surgical fields: gastric surgery, colorectal surgery, hepatectomy and liver transplantation, pancreatectomy, and kidney transplantation. Results: Common and surgery-specific complication platforms will be developed, and the program will work toward the implementation of an artificial intelligence-based complication prediction system and the provision of evidence-based feedback to participating institutions. K-QIPS represents a significant step toward improving surgical quality and patient safety in Korea. Conclusion This program aims to reduce postoperative complications, mortality, and medical costs by providing a standardized platform for complication management and prediction. The successful implementation of this nationwide project may provide a good model for other countries that are required to improve surgical outcomes and patient care.

Purpose: Improvements in surgical quality and patient safety are critical components of the healthcare system. Despite excellent cancer survival rates in Korea, there is a lack of standardized postoperative complication management systems.To address this gap, the Korean Surgical Society initiated the development of the Korean Quality Improvement Platform in Surgery (K-QIPS) program. Methods: K-QIPS was successfully launched in 87 general hospitals. This nationwide surgical quality improvement program covers 5 major surgical fields: gastric surgery, colorectal surgery, hepatectomy and liver transplantation, pancreatectomy, and kidney transplantation. Results: Common and surgery-specific complication platforms will be developed, and the program will work toward the implementation of an artificial intelligence-based complication prediction system and the provision of evidence-based feedback to participating institutions. K-QIPS represents a significant step toward improving surgical quality and patient safety in Korea. Conclusion This program aims to reduce postoperative complications, mortality, and medical costs by providing a standardized platform for complication management and prediction. The successful implementation of this nationwide project may provide a good model for other countries that are required to improve surgical outcomes and patient care.

Background: This study aimed to generate a Z score calculation model for coronary artery diameter of normal children and adolescents to be adopted as the standard calculation method with consensus in clinical practice. Methods: This study was a retrospective, multicenter study that collected data from multiple institutions across South Korea. Data were analyzed to determine the model that best fit the relationship between the diameter of coronary arteries and independent demographic parameters. Linear, power, logarithmic, exponential, and square root polynomial models were tested for best fit. Results: Data of 2,030 subjects were collected from 16 institutions. Separate calculation models for each sex were developed because the impact of demographic variables on the diameter of coronary arteries differs according to sex. The final model was the polynomial formula with an exponential relationship between the diameter of coronary arteries and body surface area using the DuBois formula. Conclusion A new coronary artery diameter Z score model was developed and is anticipated to be applicable in clinical practice. The new model will help establish a consensus-based Z score model.

Purpose: Recently, deep learning techniques have been introduced for age estimation, with automated methods based on radiographic analysis demonstrating high accuracy. In this study, we applied convolutional neural network (CNN) techniques to the lower dentition area on orthopantomograms (OPGs) of children to develop an automated age estimation model and evaluate its accuracy for use in forensic dentistry. Methods: In this study, OPGs of 2,856 subjects aged 3-14 years were analyzed. The You Only Look Once (YOLO) V8 object detection technique was applied to extract the mandibular dentition area on OPGs, designating it as the region of interest (ROI). First, 200 radiographs were randomly selected, and were used to train a model for extracting the ROI. The trained model was then applied to the entire dataset. For the CNN image classification task, 80% of OPGs were allocated to the training set, while the remaining 20% were used as the test set. A transfer learning approach was employed using the ResNet50 and VGG19 backbone models, with an ensemble technique combining these models to improve performance. The mean absolute error (MAE) on the test set was used as the validation metric, and the model with the lowest MAE was selected. Results: In this study, the age estimation model developed using mandibular dentition region from OPGs achieved MAE and root mean squared error (RMSE) values of 0.501 and 0.742, respectively, on the test set, and MAE and RMSE values of 0.273 and 0.354, respectively, on the training set. Conclusions The automated age estimation model developed in this study demonstrated accuracy comparable to that of previous research and shows potential for applications in forensic investigations. Increasing the sample size and incorporating diverse deep learning techniques are expected to further enhance the accuracy of future age estimation models.

Purpose: Improvements in surgical quality and patient safety are critical components of the healthcare system. Despite excellent cancer survival rates in Korea, there is a lack of standardized postoperative complication management systems.To address this gap, the Korean Surgical Society initiated the development of the Korean Quality Improvement Platform in Surgery (K-QIPS) program. Methods: K-QIPS was successfully launched in 87 general hospitals. This nationwide surgical quality improvement program covers 5 major surgical fields: gastric surgery, colorectal surgery, hepatectomy and liver transplantation, pancreatectomy, and kidney transplantation. Results: Common and surgery-specific complication platforms will be developed, and the program will work toward the implementation of an artificial intelligence-based complication prediction system and the provision of evidence-based feedback to participating institutions. K-QIPS represents a significant step toward improving surgical quality and patient safety in Korea. Conclusion This program aims to reduce postoperative complications, mortality, and medical costs by providing a standardized platform for complication management and prediction. The successful implementation of this nationwide project may provide a good model for other countries that are required to improve surgical outcomes and patient care.

Background: /Aim: This study aimed to determine the diagnostic performance of 2022 Korean Liver Cancer Association-National Cancer Center (KLCA-NCC) imaging criteria compared with the 2018 KLCA-NCC for hepatocellular carcinoma (HCC) in high-risk patients using magnetic resonance imaging (MRI). Methods: This retrospective study included 415 treatment-naïve patients (152 patients who underwent extracellular contrast agent [ECA]-MRI and 263 who underwent hepatobiliary agent [HBA]-MRI; 535 lesions, including 412 HCCs) with a high risk of HCC who underwent contrast-enhanced MRI. Two readers evaluated all lesions according to the 2018 and 2022 KLCA-NCC imaging diagnostic criteria, and the per-lesion diagnostic performances were compared. Results: In “definite” HCC category of both 2018 and 2022 KLCA-NCC, HBA-MRI showed a significantly higher sensitivity for the diagnosis of HCC than ECA-MRI (77.0% vs. 64.3%, P=0.006) without a significant difference in specificity (94.7% vs. 95.7%, P=0.801). On ECAMRI, “definite” or “probable” HCC categories of the 2022 KLCA-NCC had significantly higher sensitivity than those of the 2018 KLCA-NCC (85.3% vs. 78.3%, P=0.002) with identical specificity (93.6%). On HBA-MRI, the sensitivity and specificity of “definite” or “probable” HCC categories of both 2018 and 2022 KLCA-NCC were not significantly different (83.3% vs. 83.6%, P>0.999 and 92.1% vs. 90.8%, P>0.999, respectively). Conclusions In “definite” HCC category of both 2018 and 2022 KLCA-NCC, HBA-MRI provides better sensitivity than ECA-MRI without compromising specificity. On ECA-MRI, “definite” or “probable” HCC categories of the 2022 KLCA-NCC may improve sensitivity in the diagnosis of HCC compared with the 2018 KLCA-NCC.

In children and adolescents, dental age estimation is performed with the development of the teeth. Various statistical analysis methods have been used to determine the relationship between age and dental maturity and develop an accurate method of age calculation. This study attempted to apply a neural network model for the statistical analysis of dental age estimation in children and evaluated its applicability. This study used 1196 panoramic radiographs of patients aged 3–16 years, and 996 and 200 were randomly classified into training and test sets, respectively. The dental maturity of the mandibular left teeth was evaluated using Demirjian's method, the neural network model using the backpropagation algorithm was derived using training sets, and the errors were evaluated using 100 radiographs of each male and female as test sets. In addition, multiple linear regression analysis was conducted on the same training set, and the error was calculated by applying it to the test set and comparing it with the error of the neural network model. In the neural network model, the mean absolute error (MAE) and root mean squared error (RMSE) were 0.589 and 0.783 in male subjects and 0.529 and 0.760 in female subjects, respectively. In the multiple linear regression model, the MAE and RMSE were 0.600 and 0.748 in male subjects and 0.566 and 0.789 in female subjects, respectively. When applying the neural network model to the statistical analysis of the dental developmental stage, the results were as accurate as those of conventional statistical analysis methods. This study’s approach is expected to be useful for estimating the ages of children.

Background: Most pain management techniques for challenging procedures are still performed under the guidance of the C-arm fluoroscope although it is sometimes difficult for even experienced clinicians to understand the modified threedimensional anatomy as a two-dimensional X-ray image. To overcome these difficulties, the development of a virtual simulator may be helpful. Therefore, in this study, the authors developed a virtual simulator and presented its clinical application cases. Methods: We developed a computer program to simulate the actual environment of the procedure. Computed tomography (CT) Digital Imaging and Communications in Medicine (DICOM) data were used for the simulations. Virtual needle placement was simulated at the most appropriate position for a successful block. Using a virtual C-arm, the authors searched for the position of the C-arm at which the needle was visualized as a point. The positional relationships between the anatomy of the patient and the needle were identified. Results: For the simulations, the CT DICOM data of patients who visited the outpatient clinic was used. When the patients revisited the clinic, images similar to the simulated images were obtained by manipulating the C-arm. Transforaminal epidural injection, which was difficult to perform due to severe spinal deformity, and the challenging procedures of the superior hypogastric plexus block and Gasserian ganglion block, were successfully performed with the help of the simulation. Conclusions We created a pre-procedural virtual simulation and demonstrated its successful application in patients who are expected to undergo challenging procedures.

Purpose: Patients who have undergone gastrectomy have unique symptoms that are not appropriately assessed using currently available tools. This study developed and validated a symptom-focused quality of life (QoL) questionnaire for patients who have received gastrectomy for gastric cancer. Materials and Methods Based on a literature review, patient interviews, and expert consultation by the KOrean QUality of life in Stomach cancer patients Study group (KOQUSS), the initial item pool was developed. Two large-scale developmental studies were then sequentially conducted for exploratory factor analyses for content validity and item reduction. The final item pool was validated in a separate cohort of patients and assessed for internal consistency, test-retest reliability, construct validity, and clinical validity. Results The initial questionnaire consisted of 46-items in 12 domains. Data from 465 patients at 11 institutions, followed by 499 patients at 13 institutions, were used to conduct item reduction and exploratory factor analyses. The final questionnaire (KOQUSS-40) comprised 40 items within 11 domains. Validation of KOQUSS-40 was conducted on 413 patients from 12 hospitals. KOQUSS-40 was found to have good model fit. The mean summary score of the KOQUSS-40 was correlated with the EORTC QLQ-C30 and STO22 (correlation coefficients, 0.821 and 0.778, respectively). The KOQUSS-40 score was also correlated with clinical factors, and had acceptable internal consistency (> 0.7). Test-retest reliability was greater than 0.8. Conclusion The KOQUSS-40 can be used to assess QoL of gastric cancer patients after gastrectomy and allows for a robust comparison of surgical techniques in clinical trials.