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Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Launched in 1964, the Journal of the Korean Society of Radiology (JKSR) has served as the official journal of the Korean Society of Radiology (KSR). However, with establishment of the Korean Journal of Radiology (KJR), an entirely English journal, in 2000 and listing of the KJR in the SCI(E), KJR has become the representative academic journal of the KSR. Therefore, the identity of JKSR needs to be restructured, and a questionnaire survey was conducted to investigate the status and the future direction of the JKSR. The survey was conducted in September 2018 with 10 questions through on-line and mobile, and 553 members gave responses. According to the survey results, about 75% of the respondents read more than one JKSR article a year, mostly through on-line. All types of papers, including original article, case report, and review article were being read. The most common reason for contributing articles to the JKSR was submission for the radiology board examination, and the most common reason for not contributing articles to the JKSR was non-recognition of the publication in JKSR as achievement in universities because JKSR is not indexed in SCI(E). Many respondents were hoping for dealing with continuous medical education and health policies in radiology as the future role of JKSR. Based on these surveys, we plan to reorganize JKSR in 2019, which we would like to introduce in this paper. [ABSTRACT FROM AUTHOR]

Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer’s disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future. [ABSTRACT FROM AUTHOR]

Infections and inflammatory conditions of immature musculoskeletal systems in pediatric patients also affect the adjacent muscles, connective tissues, and joints. Rapid diagnosis leading to appropriate treatment can significantly impact the occurrence of complications and mortality rates due to these conditions. When a radiologist becomes familiar with the imaging findings of pediatric musculoskeletal infections and inflammatory diseases, rapid differential diagnoses and more timely and appropirate treatment could be possible. In this paper, we introduce the imaging findings of infectious and inflammatory diseases affecting the immature musculoskeletal system, such as osteomyelitis, pyogenic arthritis, juvenile idiopathic arthritis, and hemophilic arthritis, based on the anatomical and pathophysiological characteristics of the immature musculoskeletal system in children.

Acute abdomen is a condition with sudden abdominal pain that may require immediate surgical treatment. The causes of acute abdomen in pediatric patients are diverse, and can be categorized in broad range from diseases requiring surgery to diseases requiring medication or clinical observation only. The role of the imaging study in children with acute abdomen is to distinguish between patients who need medication and patients who need surgery by identifying diseases that cause abdominal pain, if possible. Since intussusception and appendicitis are the leading causes of acute abdomen requiring surgical treatment in children, it is important to exclude intussusception in young infants complaining of acute abdominal pain and exclude acute appendicitis in older children with acute abdomen. In this paper, we introduce intussusception, acute appendicitis, midgut volvulus, Meckel’s diverticulum and duplication cyst, which has characteristic imaging finding of the disease that can cause acute abdomen in pediatric patients [ABSTRACT FROM AUTHOR]

Statistical analysis is an essential component of the medical writing process for research-related articles. Although the importance of statistical testing is emphasized, statistical mistakes continue to appear in journal articles. Major statistical mistakes can occur in any of the three different stages of medical writing, including in the design stage, analysis stage, and interpretation stage. In the design stage, mistakes occur if there is a lack of specificity regarding the research hypothesis or data collection and analysis plans. Discrepancies in the analysis stage occur if the purpose of the study and characteristics of the data are not sufficiently considered, or when an inappropriate analytic procedure is followed. After performing the analysis, the results are interpreted, and an article is written. Statistical analysis mistakes can occur if the underlying methods are incorrectly written or if the results are misinterpreted. In this paper, we describe the statistical mistakes that commonly occur in medical research- related articles and provide advice with the aim to help readers reduce, resolve, and avoid these mistakes in the future.

Lung cancer is the leading cause of death from cancer worldwide. The most effective way to reduce lung cancer mortality is early detection and treatment. Two large randomized controlled trials (RCTs), the National Lung Screening Trial and the Dutch-Belgian Randomized Lung Cancer Screening Trial, showed that low-dose CT (LDCT) can reduce the chances of lung cancer death. This paper reviews the two aforementioned RCTs and the current situations of implementing LDCT screening in several counties. Although nationwide programs of lung cancer screening are rare, they would increase in the near future. Using the two aforementioned RCTs and accumulating data from many counties, including the East Asian countries, a more effective way of LDCT screening in Korea can be devised and implemented. [ABSTRACT FROM AUTHOR]

Urinary tract injuries occur in up to 10% of patients with abdominal trauma, and the kidney is the most commonly injured structure of the urinary system. The kidney is the third most common abdominal organ to be injured by trauma, following the spleen and liver, in that order. The American Association for the Surgery of Trauma kidney injury scale is the most commonly used classification system for blunt renal injuries, which grades renal injury based on the size of laceration and its proximity to the renal hilum on CT. CT aids in delineating the most important features of urological injury that affect the diagnosis and management, including interventions. The major renal injuries are usually surgical emergencies. The current trend toward a more conservative management of renal trauma and advances in interventional radiology in the field of trauma may increase the use of interventional procedures for patients with renal injury. Urethral injuries can be anterior or posterior depending on the injury site, and interventional urethral realignment plays an important role in the treatment of urethral injury and surgical repair or endoscopic urethral realignment. Therefore, in this paper, we provide a literature review and discuss the efficacy and clinical significance of intervention for urologic trauma. [ABSTRACT FROM AUTHOR]

Due to rapid developments in the deep learning model, artificial intelligence (AI) models are expected to enhance clinical diagnostic ability and work efficiency by assisting physicians. Therefore, many hospitals and private companies are competing to develop AI-based automatic diagnostic systems using medical images. In the near future, many deep learning-based automatic diagnostic systems would be used clinically. However, the possibility of adversarial attacks exploiting certain vulnerabilities of the deep learning algorithm is a major obstacle to deploying deep learning-based systems in clinical practice. In this paper, we will examine in detail the kinds of principles and methods of adversarial attacks that can be made to deep learning models dealing with medical images, the problems that can arise, and the preventive measures that can be taken against them. [ABSTRACT FROM AUTHOR]

Automated breast ultrasonography (ABUS) is a recently introduced technology. Similar to handheld ultrasound (HHUS), it is a supplementary screening test for breast cancer to be used along with mammography. It is particularly useful for detecting invasive breast cancers that may be overlooked by mammography in denser breast tissue. The use of ABUS is becoming more common because of the advantages of low operator dependency during image acquisition, high reproducibility, a wide field-of-view, and unavailability of better coronal imaging with HHUS. Consequently, there have been suggestions to extend ABUS use to diagnostic screening. Therefore, in this paper, we provide a review of the literature and discuss the usefulness and value of ABUS in breast cancer screening. [ABSTRACT FROM AUTHOR]

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases., Competing Interests: Conflicts of Interest: Gong Yong Jin has been a Section Editor of the Journal of the Korean Society of Radiology since 2024; however, he was not involved in the peer reviewer selection, evaluation, or decision process of this article. The remaining author has declared no conflicts of interest., (Copyrights © 2024 The Korean Society of Radiology.)

Infections and inflammatory conditions of immature musculoskeletal systems in pediatric patients also affect the adjacent muscles, connective tissues, and joints. Rapid diagnosis leading to appropriate treatment can significantly impact the occurrence of complications and mortality rates due to these conditions. When a radiologist becomes familiar with the imaging findings of pediatric musculoskeletal infections and inflammatory diseases, rapid differential diagnoses and more timely and appropirate treatment could be possible. In this paper, we introduce the imaging findings of infectious and inflammatory diseases affecting the immature musculoskeletal system, such as osteomyelitis, pyogenic arthritis, juvenile idiopathic arthritis, and hemophilic arthritis, based on the anatomical and pathophysiological characteristics of the immature musculoskeletal system in children., Competing Interests: Conflicts of Interest: The author has no potential conflicts of interest to disclose., (Copyrights © 2024 The Korean Society of Radiology.)

Statistical analysis is an essential component of the medical writing process for research-related articles. Although the importance of statistical testing is emphasized, statistical mistakes continue to appear in journal articles. Major statistical mistakes can occur in any of the three different stages of medical writing, including in the design stage, analysis stage, and interpretation stage. In the design stage, mistakes occur if there is a lack of specificity regarding the research hypothesis or data collection and analysis plans. Discrepancies in the analysis stage occur if the purpose of the study and characteristics of the data are not sufficiently considered, or when an inappropriate analytic procedure is followed. After performing the analysis, the results are interpreted, and an article is written. Statistical analysis mistakes can occur if the underlying methods are incorrectly written or if the results are misinterpreted. In this paper, we describe the statistical mistakes that commonly occur in medical research-related articles and provide advice with the aim to help readers reduce, resolve, and avoid these mistakes in the future., Competing Interests: Conflicts of Interest: The authors have no potential conflicts of interest to disclose., (Copyrights © 2023 The Korean Society of Radiology.)