OBJECTIVES: In this paper, we present an efficient method to visualize computed tomography (CT) datasets using ambient occlusion, which is a global illumination technique that adds depth cues to the output image. We can change the transfer function (TF) for volume rendering and generate output images in real time. METHODS: In preprocessing, the mean and standard deviation of each local vicinity are calculated. During rendering, the ambient light intensity is calculated. The calculation is accelerated on the assumption that the CT value of the local vicinity of each point follows the normal distribution. We approximate complex TF forms with a smaller number of connected line segments to achieve additional acceleration. Ambient occlusion is combined with the existing local illumination technique to produce images with depth in real time. RESULTS: We tested the proposed method on various CT datasets using hand-drawn TFs. The proposed method enabled real-time rendering that was approximately 40 times faster than the previous method. As a result of comparing the output image quality with that of the conventional method, the average signal-to-noise ratio was approximately 40 dB, and the image quality did not significantly deteriorate. CONCLUSIONS: When rendering CT images with various TFs, the proposed method generated depth-sensing images in real time.
Acceleration ; Computer Systems ; Cues ; Dataset ; Lighting ; Mathematical Computing ; Methods ; Signal-To-Noise Ratio

Purpose: The present study investigated and compared the antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) activity-predicting ability of the serum concentrations of the four interleukin (IL)-12 family cytokines including IL-23, IL-27, IL-35, and IL-39 in patients with microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA). Materials and Methods: The present study included 70 patients with MPA and GPA. Clinical and laboratory data, particularly Birmingham Vasculitis Activity Score (BVAS), at the time of blood collection were obtained. The serum concentrations of IL-23, IL-27, IL-35, and IL-37 were measured using sera stored at -80°C. Patients were divided into two groups: the upper half of BVAS (BVAS ≥12) and the lower half of BVAS (BVAS <12). Results: The serum concentrations of IL-23 and IL-27 reflected AAV activity. Patients with the upper half of BVAS exhibited significantly higher serum concentrations of IL-23 and IL-27 than those without. Patients with the serum concentrations of IL-23 ≥132.1 pg/mL or IL-27 ≥684.7 pg/mL exhibited higher frequency and risk for the upper half of BVAS than those without [relative risks (RR) 5.143 and RR 4.091, respectively]. The serum concentrations of IL-27 were associated with age ≥65 years and proteinase 3-ANCA (or C-ANCA) negativity, whereas, those of IL-23 were associated with MPA. However, the serum concentrations of IL-35 and IL-39 were not useful in predicting AAV activity in this study. Conclusion The present study is the first to demonstrate that among the various members of IL-12 family cytokines, the serum concentrations of IL-23 and IL-27 possess AAV activity-predicting ability.

Objectives: In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications. Methods: To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management. Results: The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels. Conclusions This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.