Ewha Medical Journal最新文献

{"title":"Development of automatic organ segmentation based on positron-emission tomography analysis system using Swin UNETR in breast cancer patients in Korea.","authors":"Dong Hyeok Choi, Joonil Hwang, Hai-Jeon Yoon, So Hyun Ahn","doi":"10.12771/emj.2025.00094","DOIUrl":"10.12771/emj.2025.00094","url":null,"abstract":"<p><strong>Purpose: </strong>The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region-of-interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning-based quantitative analysis method that enhances diagnostic and prognostic accuracy.</p><p><strong>Methods: </strong>We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software.</p><p><strong>Results: </strong>In a dataset of 10 patients, our method achieved an auto-segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single-ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole-organ SUV analysis.</p><p><strong>Conclusion: </strong>This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning-based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.</p>","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e30"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699945","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":"Principles of Best Practice and Transparency in Scholarly Publishing ver. 4: a Korean translation.","authors":"","doi":"10.12771/emj.2025.00318","DOIUrl":"https://doi.org/10.12771/emj.2025.00318","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e37"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699966","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":"Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of <i>Ewha Medical Journal</i>.","authors":"Hae-Sun Chung","doi":"10.12771/emj.2025.00395","DOIUrl":"https://doi.org/10.12771/emj.2025.00395","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e16"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699967","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":"Reflections on 25 hours a day at Ewha Womans University College of Medicine from August 2021 to January 2025: a dean's farewell message.","authors":"Eunhee Ha","doi":"10.12771/emj.2025.00045","DOIUrl":"https://doi.org/10.12771/emj.2025.00045","url":null,"abstract":"","PeriodicalId":41392,"journal":{"name":"Ewha Medical Journal","volume":"48 2","pages":"e20"},"PeriodicalIF":0.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699968","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":"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":"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":0.3,"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":"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}