Background: Although Korea ranks first in the suicide rate of elderly individuals, there is limited research on those who attempt suicide, with preventive measures largely based on population-based studies. We compared the demographic and clinical characteristics of elderly individuals who attempted suicide with those of younger adults who visited the emergency department after suicide attempts and identified the factors associated with lethality in the former group. Methods: Individuals who visited the emergency department after a suicide attempt from April 1, 2017, to January 31, 2020, were included. Participants were classified into two groups according to age (elderly, ≥65 years; adult, 18–64 years). Among the 779 adult patients, 123 were elderly. We conducted a chi-square test to compare the demographic and clinical features between these groups and a logistic regression analysis to identify the risk factors for lethality in the elderly group. Results: Most elderly participants were men, with no prior psychiatric history or suicide attempts, and had a higher prevalence of underlying medical conditions and attributed their attempts to physical illnesses. Being sober and planning suicide occurred more frequently in this group. In the elderly group, factors that increased the mortality rate were biological male sex (p<0.05), being accompanied by family members (p<0.05), and poisoning as a suicide method (p<0.01). Conclusion Suicide attempts in elderly individuals have different characteristics from those in younger adults and are associated with physical illness. Suicides in the former group are unpredictable, deliberate, and fatal. Therefore, tailored prevention and intervention strategies addressing the characteristics of those who are elderly and attempt suicide are required.

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer’s disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Results: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. Conclusion This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.

Purpose: To develop and evaluate a deep learning technique to automatically segment bone structures in zero echo time (ZTE) for skull magnetic resonance imaging (MRI) in children. Materials and Methods: From January to December 2021, 38 bone ZTE MRIs from infants and children (age range, 1–31 months) were collected for model development.Mask images were generated by manually segmenting the craniofacial bone using a commercial segmentation program. Among them, 35 ZTE series were used to train the three-dimensional (3D)-nnUnet deep learning model and the remaining three series were used for model validation. A temporally different dataset of 19 ZTE bone MRIs obtained in May 2022 from infants and children (age range, 3–168 months) was used to determine the model’s performance. Dice similarity coefficient was calculated for each test case.From 3D volume rendering images, segmentation accuracy, overall image quality, and visibility of cranial sutures were subjectively evaluated on a 5-point scale and compared with ground truth data from manual segmentation. Reasons for segmentation failure were analyzed using axially segmented ZTE images. Results: For the test set, the mean Dice similarity coefficient was 0.985 ± 0.019. The segmentation accuracy was lower than the ground truth without showing a statistically significant difference between the two (3.39 ± 1.11 vs. 3.73 ± 0.77, p = 0.055). The overall image quality and suture visibility showed no significant difference (3.34 ± 0.75 vs.3.42 ± 0.69, p = 0.317; 3.55 ± 0.97 vs. 3.60 ± 0.95, p = 0.157). Common reasons for low segmentation accuracy were well-pneumatized sinuses, metal artifacts, skin at the vertex level, and bones too thin. Conclusion The deep learning-based automatic segmentation technique of bone ZTE MRIs showed comparable segmentation performance to manual segmentation. Using the deep learning-based segmentation results, acceptable 3D-volume rendering images of craniofacial bones were generated.

Objective: To determine whether dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) can be used to evaluate posterior cerebral circulation in pediatric patients with moyamoya disease (MMD) who underwent anterior revascularization. Materials and Methods: This study retrospectively included 73 patients with MMD who underwent DSC perfusion MRI (age, 12.2 ± 6.1 years) between January 2016 and December 2020, owing to recent-onset clinical symptoms during the follow-up period after completion of anterior revascularization. DSC perfusion images were analyzed using a dedicated software package (NordicICE; Nordic NeuroLab) for the middle cerebral artery (MCA), posterior cerebral artery (PCA), and posterior border zone between the two regions (PCA-MCA). Patients were divided into two groups; the PCA stenosis group included 30 patients with newly confirmed PCA involvement, while the no PCA stenosis group included 43 patients without PCA involvement. The relationship between DSC perfusion parameters and PCA stenosis, as well as the performance of the parameters in discriminating between groups, were analyzed. Results: In the PCA stenosis group, the mean follow-up duration was 5.3 years after anterior revascularization, and visual disturbances were a common symptom. Normalized cerebral blood volume was increased, and both the normalized time-topeak (nTTP) and mean transit time values were significantly delayed in the PCA stenosis group compared with those in the no PCA stenosis group in the PCA and PCA-MCA border zones. TTP PCA (odds ratio [OR] = 6.745; 95% confidence interval [CI] = 2.665–17.074; P < 0.001) and CBVPCA-MCA (OR = 1.567; 95% CI = 1.021–2.406; P = 0.040) were independently associated with PCA stenosis. TTPPCA showed the highest receiver operating characteristic curve area in discriminating for PCA stenosis (0.895; 95% CI = 0.803–0.986). Conclusion nTTP can be used to effectively diagnose PCA stenosis. Therefore, DSC perfusion MRI may be a valuable tool for monitoring PCA stenosis in patients with MMD.

Objective: This study aimed to explore the characteristics and factors related to changes in cognitive function in vulnerable individuals with cognitive impairment during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Among patients who visited a local university hospital with subjective cognitive complaints, those who had been tested for cognitive function at least once after the onset of COVID-19 and tested regularly at least three times within the last 5 years were included (1st, the initial screening; 2nd, the test immediately before the COVID-19 pandemic; 3rd, the most recent test after the pandemic). Finally, 108 patients were included in this study. They were divided into groups according to whether the Clinical Dementia Rating (CDR) was maintained/improved and deteriorated. We investigated the characteristics of the changes in cognitive function and related factors during COVID-19. Results: When comparing CDR changes before and after COVID-19, there was no significant difference between the two groups (p=0.317). Alternatively, the main effect of the time when the test was conducted was significant (p<0.001). There was also a significant difference in the interaction between the groups and time. When the effect of the interaction was analyzed, the CDR score of the maintained/ improved group significantly decreased before COVID-19 (1st–2nd) (p=0.045). After COVID-19 (2nd–3rd), the CDR score of the deteriorated group was significantly higher than that of the maintained/improved group (p<0.001). Mini-Mental State Examination recall memory and changes in activity during COVID-19 were significantly associated with CDR deterioration. Conclusion Memory dysfunction and decreased activity during the COVID-19 pandemic are strongly related to the deterioration of cognitive impairment.

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%– 94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850). Conclusion A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

Background/Aims: The eradication success rate of Helicobacter pylori (H. pylori) infection with a first-line standard triple therapy (STT) has been decreasing in Korea. However, treatment outcomes of H. pylori infection in Yeongdong, Gangwon Province have been scarcely reported. This study aimed to investigate the treatment outcomes of H. pylori infection in a single tertiary care hospital with regional characteristics. Materials and Methods: From July 2018 to June 2019, a total of 592 patients who underwent STT consisting of a proton pump inhibitor, amoxicillin, and clarithromycin for 7 to 14 days as a first-line H. pylori eradication therapy were included. Demographic data and treatment outcomes were retrospectively reviewed using medical records. Results: The median age of 592 patients was 58 years (range 23 to 86) and 329 patients (55.6%) were men. The indication for eradication therapy included chronic atrophic gastritis (57.9%), peptic ulcer disease (19.6%), and gastric neoplasm after endoscopic resection (5.9%). Most patients (92.2%) received a 7-day course of STT. Eradication rate of STT was 64.0% (379/592). Rescue therapy was performed in 146 patients, and the final eradication rate reached 85.6% (507/592). Conclusions Eradication rate of STT in Yeongdong area of Gangwon Province was unsatisfactory, warranting the consideration of a first-line eradication regimen other than STT.

Background: Patient-centered management is becoming increasingly important in gout, but there are limited studies exploring patients' perspectives and preferences. We aimed to investigate patients' perspectives and preferences regarding gout and gout management, and their impacts on adherence to urate lowering therapy (ULT). Methods: A paper-based survey was performed in patients with gout seen at the rheumatology outpatient clinics of 16 tertiary hospitals. The survey included questions regarding demographics, comorbidities, gout attacks, current treatment and adherence, and patients' perspectives and preferences regarding gout and gout management. Multivariate regression analysis was performed to determine the factors associated with ULT adherence. Results: Of 809 surveyed patients with gout, 755 (94.5%) were using ULT. Among those using ULT, 89.1% had ≥ 80% adherence to ULT. Majority of the patients knew management strategies to some extent (94.8%), perceived gout as a life-long disease (91.2%), and were making efforts toward practicing at least one lifestyle modification (89.2%). Most patients (71.9%) obtained information about gout management during their clinic visits.Approximately half of the patients (53.6%) preferred managing their disease with both ULT and lifestyle modification, 28.4% preferred ULT only, and 17.4% preferred lifestyle modification only. Adherence was better in patients with older age (odds ratio [OR], 1.03), those with better knowledge of gout management strategies (OR, 3.56), and those who had preference for ULT (OR, 2.07). Conclusion Patients' perspectives and management preferences had high impacts on adherence to ULT in gout. Consideration of patients' perspectives and preferences is important for achieving the desired clinical outcome in gout.

Background: Patient-centered management is becoming increasingly important in gout, but there are limited studies exploring patients' perspectives and preferences. We aimed to investigate patients' perspectives and preferences regarding gout and gout management, and their impacts on adherence to urate lowering therapy (ULT). Methods: A paper-based survey was performed in patients with gout seen at the rheumatology outpatient clinics of 16 tertiary hospitals. The survey included questions regarding demographics, comorbidities, gout attacks, current treatment and adherence, and patients' perspectives and preferences regarding gout and gout management. Multivariate regression analysis was performed to determine the factors associated with ULT adherence. Results: Of 809 surveyed patients with gout, 755 (94.5%) were using ULT. Among those using ULT, 89.1% had ≥ 80% adherence to ULT. Majority of the patients knew management strategies to some extent (94.8%), perceived gout as a life-long disease (91.2%), and were making efforts toward practicing at least one lifestyle modification (89.2%). Most patients (71.9%) obtained information about gout management during their clinic visits.Approximately half of the patients (53.6%) preferred managing their disease with both ULT and lifestyle modification, 28.4% preferred ULT only, and 17.4% preferred lifestyle modification only. Adherence was better in patients with older age (odds ratio [OR], 1.03), those with better knowledge of gout management strategies (OR, 3.56), and those who had preference for ULT (OR, 2.07). Conclusion Patients' perspectives and management preferences had high impacts on adherence to ULT in gout. Consideration of patients' perspectives and preferences is important for achieving the desired clinical outcome in gout.