Objective: The aim of this study was to analyze the origins of prostatic arteries (PAs) in the Korean population and compare them with those reported in the literature. Materials and Methods: From April 2018 to February 2024, 108 male (mean age ± standard deviation: 71.6 ± 9.7 years) with lower urinary tract symptoms (n = 102) or refractory hematuria (n = 6) underwent prostatic artery embolization (PAE). Computed tomography and angiography images were retrospectively reviewed. The branching pattern of the internal iliac artery (IIA) was classified according to the Yamaki system. The origin of the PA was categorized using the de Assis definition, and the incidence of each type was recorded. A systematic literature review was conducted and the most common types of PA were investigated. Results: PAE was successfully implemented on 211 of the 216 pelvic sidewalls. PA cannulation failed in five sidewalls due to a steno-occlusive state. The most common IIA type was type A, in which the IIA was divided into the superior gluteal artery and gluteal–pudendal trunk (77%). Of 226 PAs analyzed, including 15 in 211 sidewalls exhibiting dual PAs, the most common PA origin was the internal pudendal artery (type IV, 35%), followed by the superior vesical (type I, 25%) and obturator (type III, 21%) arteries. Anterior division of IIA (type II) was less common (10%). Type V (uncommon origins) occurred in 8% of cases, including five distal internal pudendal arteries, four quadfurcations, three inferior gluteal arteries, three trifurcations, two medial femoral circumflex arteries, and two rectal arteries. Two of the five patients with surgically or endovascularly altered anatomy were successfully treated via PAs originating from the medial femoral circumflex arteries. Globally, type I is the most common PA type. Conclusion In the Korean population, the most common IIA pattern and PA origin were types A and IV, respectively.

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.

Purpose: This study aimed to determine whether zero echo time magnetic resonance imaging (ZTE-MRI), as an alternative imaging modality, and conventional computed tomography (CT) have similar diagnostic qualities for assessing pediatric osseous pathologies. Materials and Methods: Twenty-six sets of pediatric musculoskeletal CT and MRI scans (15 boys and 11 girls; mean age, 12 ± 4 years; range, 5–23 years) acquired at Seoul National University Children’s Hospital (January 2021 to November 2023) were retrospectively evaluated. CT-like images from ZTE-MRI were generated using grayscale inversion. Two radiologists independently assessed ZTE-MRI image quality (S anat) on a 5-point scale (1 = nondiagnostic, 5 = excellent) and a comparative scale (–2 = CT greater, 0 = same, 2 = ZTE-MRI greater) for lesion delineation (Scomp). The confidence interval of proportions and intraclass correlation coefficient were calculated to assess inter-rater agreement, and Wilcoxon rank-sum test, Mann–Whitney U test, or paired t-test was used to compare image quality or cortical thickness between the modalities. Results: ZTE-MRI demonstrated diagnostic quality (S anat ≥ 3) in 85%–96% of the cases, 89%–96% for cortical delineation, 92%–100% for intramedullary cavity (IMC) delineation, and 92% for lesion delineation. Compared with conventional CT, ZTE-MRI showed comparable diagnostic power (Scomp ≥ –1) in 92%–96% of the cases, with Scomp scores indicating no significant difference in lesion delineation (p = 0.53 in reader 1 and p = 0.25 in reader 2). There was a preference for CT over ZTE-MRI in terms of overall image quality and delineation of the cortex and IMC (p < 0.001). Cortical thickness was not significantly different (p = 0.11) between ZTE-MRI and CT. Conclusion ZTE-MRI demonstrated diagnostic quality comparable to that of CT, particularly in lesion delineation. In addition to the unique information that conventional MRI can provide, ZTE-MRI can provide additional information about osseous structures similar to that provided by CT, which we believe will be valuable in the future.

Objective: The aim of this study was to analyze the origins of prostatic arteries (PAs) in the Korean population and compare them with those reported in the literature. Materials and Methods: From April 2018 to February 2024, 108 male (mean age ± standard deviation: 71.6 ± 9.7 years) with lower urinary tract symptoms (n = 102) or refractory hematuria (n = 6) underwent prostatic artery embolization (PAE). Computed tomography and angiography images were retrospectively reviewed. The branching pattern of the internal iliac artery (IIA) was classified according to the Yamaki system. The origin of the PA was categorized using the de Assis definition, and the incidence of each type was recorded. A systematic literature review was conducted and the most common types of PA were investigated. Results: PAE was successfully implemented on 211 of the 216 pelvic sidewalls. PA cannulation failed in five sidewalls due to a steno-occlusive state. The most common IIA type was type A, in which the IIA was divided into the superior gluteal artery and gluteal–pudendal trunk (77%). Of 226 PAs analyzed, including 15 in 211 sidewalls exhibiting dual PAs, the most common PA origin was the internal pudendal artery (type IV, 35%), followed by the superior vesical (type I, 25%) and obturator (type III, 21%) arteries. Anterior division of IIA (type II) was less common (10%). Type V (uncommon origins) occurred in 8% of cases, including five distal internal pudendal arteries, four quadfurcations, three inferior gluteal arteries, three trifurcations, two medial femoral circumflex arteries, and two rectal arteries. Two of the five patients with surgically or endovascularly altered anatomy were successfully treated via PAs originating from the medial femoral circumflex arteries. Globally, type I is the most common PA type. Conclusion In the Korean population, the most common IIA pattern and PA origin were types A and IV, respectively.

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.

Objective: The aim of this study was to analyze the origins of prostatic arteries (PAs) in the Korean population and compare them with those reported in the literature. Materials and Methods: From April 2018 to February 2024, 108 male (mean age ± standard deviation: 71.6 ± 9.7 years) with lower urinary tract symptoms (n = 102) or refractory hematuria (n = 6) underwent prostatic artery embolization (PAE). Computed tomography and angiography images were retrospectively reviewed. The branching pattern of the internal iliac artery (IIA) was classified according to the Yamaki system. The origin of the PA was categorized using the de Assis definition, and the incidence of each type was recorded. A systematic literature review was conducted and the most common types of PA were investigated. Results: PAE was successfully implemented on 211 of the 216 pelvic sidewalls. PA cannulation failed in five sidewalls due to a steno-occlusive state. The most common IIA type was type A, in which the IIA was divided into the superior gluteal artery and gluteal–pudendal trunk (77%). Of 226 PAs analyzed, including 15 in 211 sidewalls exhibiting dual PAs, the most common PA origin was the internal pudendal artery (type IV, 35%), followed by the superior vesical (type I, 25%) and obturator (type III, 21%) arteries. Anterior division of IIA (type II) was less common (10%). Type V (uncommon origins) occurred in 8% of cases, including five distal internal pudendal arteries, four quadfurcations, three inferior gluteal arteries, three trifurcations, two medial femoral circumflex arteries, and two rectal arteries. Two of the five patients with surgically or endovascularly altered anatomy were successfully treated via PAs originating from the medial femoral circumflex arteries. Globally, type I is the most common PA type. Conclusion In the Korean population, the most common IIA pattern and PA origin were types A and IV, respectively.

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.