Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Background: The optimal duration and net clinical benefit of dual antiplatelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR) have not been elucidated in realworld situations. Methods: Using nationwide claims data from 2013 to 2021, we selected patients who underwent TAVR and categorized them into two groups: short- and long-term (≤ 3 and > 3 months, respectively) DAPT group. Propensity score matching was used to balance baseline characteristics. The primary endpoint was the occurrence of net adverse clinical events (NACEs), including all-cause death, myocardial infarction, stroke, any coronary and peripheral revascularization, systemic thromboembolism, and bleeding events, at 1 year. Survival analyses were conducted using Kaplan-Meier estimation and Cox proportional hazards regression. Results: Patients who met the inclusion criteria (1,695) were selected. Propensity score matching yielded 1,215 pairs of patients: 416 and 799 in the short- and long-term DAPT groups, respectively. In the unmatched cohort, the mean ages were 79.8 ± 6.1 and 79.7 ± 5.8 years for the short- and long-term DAPT groups, respectively. In the matched cohort, the mean ages were 80.6 ± 5.9 and 79.9 ± 5.9 years for the short- and long-term DAPT groups, respectively. Over one year in the unmatched cohort, the NACE incidence was 11.9% and 11.5% in the short- and long-term DAPT groups, respectively (P = 0.893). The all-cause mortality rates were 7.4% and 4.7% (P = 0.042), composite ischemic event rates were 2.5% and 4.7% (P = 0.056), and bleeding event rates were 2.7% and 4.7% (P = 0.056) in the shortand long-term groups, respectively. In the matched cohort, the incidence of NACE was 9.6% in the short-term DAPT group and 11.6% in the long-term DAPT group, respectively (P = 0.329).The all-cause mortality rates were 6.5% and 4.9% (P = 0.298), composite ischemic event rates were 1.4% and 4.5% (P = 0.009), and bleeding event rates were 2.2% and 4.4% (P = 0.072) in the short- and long-term groups, respectively. Conclusion In patients who successfully underwent transfemoral TAVR, the short- and longterm DAPT groups exhibited similar one-year NACE rates. However, patients in the long-term DAPT group experienced more bleeding and ischemic events.

Background: The optimal duration and net clinical benefit of dual antiplatelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR) have not been elucidated in realworld situations. Methods: Using nationwide claims data from 2013 to 2021, we selected patients who underwent TAVR and categorized them into two groups: short- and long-term (≤ 3 and > 3 months, respectively) DAPT group. Propensity score matching was used to balance baseline characteristics. The primary endpoint was the occurrence of net adverse clinical events (NACEs), including all-cause death, myocardial infarction, stroke, any coronary and peripheral revascularization, systemic thromboembolism, and bleeding events, at 1 year. Survival analyses were conducted using Kaplan-Meier estimation and Cox proportional hazards regression. Results: Patients who met the inclusion criteria (1,695) were selected. Propensity score matching yielded 1,215 pairs of patients: 416 and 799 in the short- and long-term DAPT groups, respectively. In the unmatched cohort, the mean ages were 79.8 ± 6.1 and 79.7 ± 5.8 years for the short- and long-term DAPT groups, respectively. In the matched cohort, the mean ages were 80.6 ± 5.9 and 79.9 ± 5.9 years for the short- and long-term DAPT groups, respectively. Over one year in the unmatched cohort, the NACE incidence was 11.9% and 11.5% in the short- and long-term DAPT groups, respectively (P = 0.893). The all-cause mortality rates were 7.4% and 4.7% (P = 0.042), composite ischemic event rates were 2.5% and 4.7% (P = 0.056), and bleeding event rates were 2.7% and 4.7% (P = 0.056) in the shortand long-term groups, respectively. In the matched cohort, the incidence of NACE was 9.6% in the short-term DAPT group and 11.6% in the long-term DAPT group, respectively (P = 0.329).The all-cause mortality rates were 6.5% and 4.9% (P = 0.298), composite ischemic event rates were 1.4% and 4.5% (P = 0.009), and bleeding event rates were 2.2% and 4.4% (P = 0.072) in the short- and long-term groups, respectively. Conclusion In patients who successfully underwent transfemoral TAVR, the short- and longterm DAPT groups exhibited similar one-year NACE rates. However, patients in the long-term DAPT group experienced more bleeding and ischemic events.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.