Ewha Medical Journal最新文献

{"title":"Comparative evaluation of deep learning architectures, including UNet, TransUNet, and MIST, for left atrium segmentation in cardiac computed tomography of congenital heart diseases.","authors":"Seoyeong Yun, Jooyoung Choi","doi":"10.12771/emj.2025.00087","DOIUrl":"10.12771/emj.2025.00087","url":null,"abstract":"<p><strong>Purpose: </strong>This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95.</p><p><strong>Methods: </strong>We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance.</p><p><strong>Results: </strong>MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67-0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model's performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation.</p><p><strong>Conclusion: </strong>MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e33"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ewha leading the era of great transformation through inclusive innovation for a sustainable future: a presidential inaugural address.","authors":"Hyang-Sook Lee","doi":"10.12771/emj.2025.00108","DOIUrl":"https://doi.org/10.12771/emj.2025.00108","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e18"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging science and policy in tuberculosis treatment through innovations in precision medicine, drug development, and cohort research: a narrative review.","authors":"Jinsoo Min, Bruno B Andrade, Ju Sang Kim, Yoolwon Jeong","doi":"10.12771/emj.2025.00115","DOIUrl":"10.12771/emj.2025.00115","url":null,"abstract":"<p><p>Recent advancements in tuberculosis treatment research emphasize innovative strategies that enhance treatment efficacy, reduce adverse effects, and adhere to patient-centered care principles. As tuberculosis remains a significant global health challenge, integrating new and repurposed drugs presents promising avenues for more effective management, particularly against drug-resistant strains. Recently, the spectrum concept in tuberculosis infection and disease has emerged, underscoring the need for research aimed at developing treatment plans specific to each stage of the disease. The application of precision medicine to tailor treatments to individual patient profiles is crucial for addressing the diverse and complex nature of tuberculosis infections. Such personalized approaches are essential for optimizing therapeutic outcomes and improving patient adherence-both of which are vital for global tuberculosis eradication efforts. The role of tuberculosis cohort studies is also emphasized, as they provide critical data to support the development of these tailored treatment plans and deepen our understanding of disease progression and treatment response. To advance these innovations, a robust tuberculosis policy framework is required to foster the integration of research findings into practice, ensuring that treatment innovations are effectively translated into improved health outcomes worldwide.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e22"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current and emerging treatment strategies for <i>Mycobacterium avium</i> complex pulmonary disease: a narrative review.","authors":"Chiwook Chung","doi":"10.12771/emj.2025.00080","DOIUrl":"10.12771/emj.2025.00080","url":null,"abstract":"<p><p>The <i>Mycobacterium avium</i> complex (MAC), comprising <i>M. avium</i> and <i>M. intracellulare</i>, constitutes the predominant cause of nontuberculous mycobacterial pulmonary disease (NTM-PD) in Korea, followed by the <i>M. abscessus</i> complex. Its global prevalence is increasing, as shown by a marked rise in Korea from 11.4 to 56.7 per 100,000 individuals between 2010 and 2021, surpassing the incidence of tuberculosis. Among the older adult population (aged ≥65 years), the prevalence escalated from 41.9 to 163.1 per 100,000, accounting for 47.6% of cases by 2021. Treatment should be individualized based on prognostic indicators, including cavitary disease, low body mass index, and positive sputum smears for acid-fast bacilli. Current therapeutic guidelines recommend a 3-drug regimen-consisting of a macrolide, rifampin, and ethambutol-administered for a minimum of 12 months following culture conversion. Nevertheless, treatment success rates are only roughly 60%, and over 30% of patients experience recurrence. This is often attributable to reinfection rather than relapse. Antimicrobial susceptibility testing for clarithromycin and amikacin is essential, as resistance significantly worsens prognosis. Ethambutol plays a crucial role in preventing the development of macrolide resistance, whereas the inclusion of rifampin remains a subject of ongoing debate. Emerging therapeutic strategies suggest daily dosing for milder cases, increased azithromycin dosing, and the substitution of rifampin with clofazimine in severe presentations. Surgical resection achieves a notable sputum conversion rate of approximately 93% in eligible candidates. For refractory MAC-PD, adjunctive therapy with amikacin is advised, coupled with strategies to reduce environmental exposure. Despite advancements in therapeutic approaches, patient outcomes remain suboptimal, highlighting the urgent need for novel interventions.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e25"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dementia-related death statistics in Korea between 2013 and 2023.","authors":"Seokmin Lee","doi":"10.12771/emj.2025.00304","DOIUrl":"10.12771/emj.2025.00304","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to analyze dementia-related death statistics in Korea between 2013 and 2023.</p><p><strong>Methods: </strong>The analysis utilized microdata from Statistics Korea's cause-of-death statistics. Among all recorded deaths, those related to dementia were extracted and analyzed using the underlying cause-of-death codes from the International Classification of Diseases, 10th revision.</p><p><strong>Results: </strong>The number of dementia-related deaths increased from 8,688 in 2013 to 14,402 in 2023. The crude death rate rose from 17.2 per 100,000 in 2013 to 28.2 per 100,000 in 2023, although the age-standardized death rate declined from 9.7 to 8.7 over the same period. The dementia death rate is 2.1 times higher in women than in men, and mortality among individuals aged 85 and older exceeds 976 per 100,000. By specific cause, Alzheimer's disease accounted for 77.1% of all dementia deaths, and by place, the majority occurred in hospitals (76.2%), followed by residential institutions including nursing homes (15.3%) in 2023.</p><p><strong>Conclusion: </strong>The rising mortality associated with dementia, especially Alzheimer's disease, highlights a growing public health concern in Korea. These findings support the need for enhanced prevention efforts, improved quality of care, and targeted policies addressing the complexities of dementia management. It is anticipated that this empirical analysis will contribute to reducing the social burden.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e35"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclic dual latent discovery for improved blood glucose prediction through patient-provider interaction modeling: a prediction study.","authors":"Suyeon Park, Seoyoung Kim, Dohyoung Rim","doi":"10.12771/emj.2025.00332","DOIUrl":"10.12771/emj.2025.00332","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient-provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient-provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy.</p><p><strong>Methods: </strong>ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient-provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers.</p><p><strong>Results: </strong>CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient-provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches.</p><p><strong>Conclusion: </strong>Integrating patient-provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e34"},"PeriodicalIF":0.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ewha, where medical education for women began in Korea, is now leading global healthcare through innovative research networks and education: the inaugural address of the 28th Dean of the College of Medicine.","authors":"Duk-Hee Kang","doi":"10.12771/emj.2025.00129","DOIUrl":"https://doi.org/10.12771/emj.2025.00129","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e19"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature-based ensemble modeling for addressing diabetes data imbalance using the SMOTE, RUS, and random forest methods: a prediction study.","authors":"Younseo Jang","doi":"10.12771/emj.2025.00353","DOIUrl":"https://doi.org/10.12771/emj.2025.00353","url":null,"abstract":"<p><strong>Purpose: </strong>This study developed and evaluated a feature-based ensemble model integrating the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) methods with a random forest approach to address class imbalance in machine learning for early diabetes detection, aiming to improve predictive performance.</p><p><strong>Methods: </strong>Using the Scikit-learn diabetes dataset (442 samples, 10 features), we binarized the target variable (diabetes progression) at the 75th percentile and split it 80:20 using stratified sampling. The training set was balanced to a 1:2 minority-to-majority ratio via SMOTE (0.6) and RUS (0.66). A feature-based ensemble model was constructed by training random forest classifiers on 10 two-feature subsets, selected based on feature importance, and combining their outputs using soft voting. Performance was compared against 13 baseline models, using accuracy and area under the curve (AUC) as metrics on the imbalanced test set.</p><p><strong>Results: </strong>The feature-based ensemble model and balanced random forest both achieved the highest accuracy (0.8764), followed by the fully connected neural network (0.8700). The ensemble model had an excellent AUC (0.9227), while k-nearest neighbors had the lowest accuracy (0.8427). Visualizations confirmed its superior discriminative ability, especially for the minority (high-risk) class, which is a critical factor in medical contexts.</p><p><strong>Conclusion: </strong>Integrating SMOTE, RUS, and feature-based ensemble learning improved classification performance in imbalanced diabetes datasets by delivering robust accuracy and high recall for the minority class. This approach outperforms traditional resampling techniques and deep learning models, offering a scalable and interpretable solution for early diabetes prediction and potentially other medical applications.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e32"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunogenicity of <i>Anisakis</i> larvae molting membrane against human eosinophilia sera.","authors":"Sooji Hong, Bong-Kwang Jung, Hyun-Jong Yang","doi":"10.12771/emj.2025.00311","DOIUrl":"10.12771/emj.2025.00311","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate whether proteins present in the molting membranes of third-stage (L3) <i>Anisakis</i> larvae could serve as potential risk factors for allergic reactions.</p><p><strong>Methods: </strong>Third-stage larvae (L3) of <i>Anisakis</i> spp. were primarily collected from mackerels and cultured in vitro to yield both molting membranes and fourth-stage (L4) larvae. Major soluble proteins in the molting membranes were identified using SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis). Crude antigens extracted from L3, L4, and the molting membranes were subsequently evaluated by western blotting using sera from <i>Anisakis</i>-infected rabbits and patients with eosinophilia.</p><p><strong>Results: </strong>Antigens derived from the molting membranes reacted with sera from <i>Anisakis</i>-infected rabbits as well as with sera from 7 patients with eosinophilia of unknown origin. These findings suggest that unidentified proteins in the molting membranes of <i>Anisakis</i> L3 may contribute to early allergic reactions, particularly in patients sensitized by specific molecular components.</p><p><strong>Conclusion: </strong>Our results indicate that proteins present in the molting membranes of third-stage <i>Anisakis</i> spp. larvae may be associated with allergic responses. Further studies are required to confirm the correlation between these membranes and <i>Anisakis</i>-induced allergies.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e29"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study.","authors":"Ji Yoon Kim","doi":"10.12771/emj.2025.00297","DOIUrl":"10.12771/emj.2025.00297","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. We propose a framework that integrates SHAP for feature selection, construction, and weighting to enhance accuracy and clinical relevance.</p><p><strong>Methods: </strong>Data from the Kaggle Appendix Cancer Prediction dataset (260,000 samples, 21 features) were used in this prediction study conducted from January through March 2025, in accordance with TRIPOD-AI guidelines. Preprocessing involved label encoding, SMOTE (synthetic minority over-sampling technique) to address class imbalance, and an 80:20 train-test split. Baseline models (random forest, XGBoost, LightGBM) were compared; LightGBM was selected for its superior performance (accuracy=0.8794). SHAP analysis identified key features and guided 3 engineering steps: selection of the top 15 features, construction of interaction-based features (e.g., chronic severity), and feature weighting based on SHAP values. Performance was evaluated using accuracy, precision, recall, and F1-score.</p><p><strong>Results: </strong>Four LightGBM model configurations were evaluated: baseline (accuracy=0.8794, F1-score=0.8691), feature selection (accuracy=0.8968, F1-score=0.8860), feature construction (accuracy=0.8980, F1-score=0.8872), and feature weighting (accuracy=0.8986, F1-score=0.8877). SHAP-based engineering yielded performance improvements, with feature weighting achieving the highest precision (0.9940). Key features (e.g., red blood cell count and chronic severity) contributed to predictions while maintaining interpretability.</p><p><strong>Conclusion: </strong>The SHAP-based framework substantially improved the accuracy and transparency of appendix cancer predictions using LightGBM (F1-score=0.8877). This approach bridges the gap between predictive power and clinical interpretability, offering a scalable model for rare disease prediction. Future validation with real-world data is recommended to ensure generalizability.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e31"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}