Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Thelazia callipaeda (Spirurida, Thelaziidae), a parasitic eye worm causing thelaziasis has been reported in humans and dogs in Korea. However, its occurrence in other potential reservoir hosts, including wild animals, remains unclear. In the present study, we described the 2 cases of thelaziasis from both of feral and captive wild animal in Korea. In August and November 2017, 2 cases of the parasitic infections were found in the third eyelid of rescued leopard cat Prionailurus bengalensis and reared raccoon dog Nyctereutes procyonoides at the Chungnam Wild Animal Rescue Center. A total of 20 and 24 worms were detected from the left and right eyes of leopard cat, respectively. In the left eye of the raccoon dog, 5 worms were recognized. Male worms were 969–11,860 μm long (10,600 μm on average) and 300–320 μm width (315 μm on average). Female worms were 13,430–15,330 (14,480) μm long and 320–370 (344) μm wide in size. They commonly had a characteristic scalariform buccal cavity and short esophagus. The vulva openings were located at the anterior of esophago-intestinal junction in females. The thelaziasis is reported in 2 species of wildlife, P. bengalensis and N. procyonoides, for the first time in Korea.

Objective: This study aims to understand the structure of meaning in life among patients with cancer through the validation of the Meaning in Life Scale among Korean patients (K-MiLS) with cancer. Methods: From August 2021 to November 2022, participants were recruited from multiple sites in South Korea. Participants completed related questionnaires, including the MiLS, on the web or mobile. Test-retest reliability was assessed between 2 and 4 weeks after the initial assessment. Exploratory and confirmatory factor analyses and Pearson’s correlations were used to evaluate the reliability and validity of the MiLS. A multiple regression analysis was conducted to examine the sociodemographic and disease-related variables correlated with the MiLS. Regarding concurrent validity, a hierarchical regression analysis was performed. Results: The results (n=345) indicated that the K-MiLS has a four-factor structure: Harmony and Peace; Life Perspective, Purpose, and Goals; Confusion and Lessened Meaning; and Benefits of Spirituality. Regarding convergent and discriminant validity, K-MiLS was negatively correlated with Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Fear of Cancer Recurrence Inventory while showing a significantly positive correlation with the Posttraumatic Growth Inventory, Self-Compassion Scale, Functional Assessment of Cancer Therapy-General, and Functional Social Support Questionnaire. Hierarchical regression analysis revealed that the demographic variable influencing MiLS was religious affiliation. Conclusion The K-MiLS had a multidimensional four-factor structure similar to that of the original version. It is also a reliable and valid measure for assessing cancer survivors’ meaning in life after a cancer diagnosis.