Background: Melanoma, though less common than other cutaneous malignancies, remains clinically significant. In Asia, acral and nailunit melanoma—less related to ultraviolet exposure—pose diagnostic and reconstructive challenges. Clarifying temporal and anatomic trends in melanoma within plastic surgery practice may enhance early recognition and guide standardized reconstruction. Methods: We retrospectively reviewed 960 surgically treated cutaneous malignancies (2000–2022) in a tertiary plastic surgery department, classifying tumors as basal cell carcinoma (BCC), squamous cell carcinoma (SCC), malignant melanoma (MM), or others. For MM, we analyzed anatomic site (headeck, trunk, non-acral extremity, acral), sex, age, comorbidities, and lifestyle factors, comparing period A (2000–2017) with period B (2018–2022). Group comparisons used the chi-square or Fisher exact test and the Mann-Whitney test. Incidence rates were calculated with Poisson confidence intervals; between-period differences were evaluated using exact binomial tests and rate ratios. Results: Of 960 tumors, BCC, SCC, MM, and others comprised 47.4%, 44.3%, 5.8%, and 2.5%. MM site distribution was heterogeneous: headeck 14.3%, trunk 30.4%, non-acral extremity 21.4%, acral 33.9%. Distribution shifted significantly (chi-square p= 0.043), with headeck lesions decreasing from 28.0% to 3.2% and trunk and acral lesions each increasing to 38.7%. Annual MM incidence rose from 1.39 to 6.20 cases per year (rate ratio, 4.46; p< 0.001). Hypertension (64.5%) and diabetes (35.5%) were more frequent in period B. Conclusion Recent years showed a sharply increased MM caseload and redistribution toward trunk and acral sites with greater metabolic comorbidity, reflecting both epidemiologic change and evolving detection or referral patterns.

Pediatric vulvar hematoma typically results from straddle-type injuries and is usually managed with nonoperative measures. However, rapid progression and the risk of complications in some cases, including tissue necrosis, infection, and urinary retention, necessitate surgical intervention. The highly vascularized anatomy of the vulva allows for rapid hematoma expansion, requiring careful assessment of hematoma size, progression, functional impairment, and hemodynamic stability when determining the treatment strategy. In addition, genital trauma during developmental periods can influence self-perception and psychosexual development, making timely intervention important to prevent aesthetic and functional sequelae. We present two pediatric patients with vulvar hematoma who underwent successful surgical treatment following careful evaluation of both physical and psychological factors. This report emphasizes the importance of a comprehensive, multifaceted approach to the management of pediatric vulvar hematomas.

Fingertip amputation is a common traumatic injury which can be treated with revascularization therapy or composite grafting. This article reports two case studies showing the successful management of fingertip amputation using hyperbaric oxygen therapy (HBOT) and prostaglandin E1 (PGE1) treatment after composite grafting, where revascularization was not possible. HBOT was used to promote angiogenesis, improve oxygen transfer, and accelerate wound healing. At the same time, PGE1 was administered to control inflammation, stimulate cell proliferation, and promote tissue repair. These case reports offer effective approaches to treating fingertip amputation. The treatment strategy used in this study can be expected to improve patient outcomes and quality of life.

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: Wound healing is a complex process influenced by a variety of environmental factors. Dressing materials play a critical role in creating barriers against contaminants, maintaining optimal moisture levels, and absorbing wound exudate. Therefore, selecting materials tailored to wound characteristics is crucial for enhancing outcomes. Hyaluronic acid (HA) is a natural biocompatible polymer that supports healing by regulating inflammation and promoting tissue repair. This study compared HA- and hydrocolloid-based hydrogels in a rat model to optimize wound care strategies. Methods: Full-thickness dermal wounds (diameter, 8 mm) were created on the dorsal skin of 12 Sprague-Dawley rats under sevoflurane anesthesia. The wounds were treated with HA/silver sulfadiazine gel (group A), hydrocolloid gel (group B), or left untreated (control), all covered with a transparent dressing. Biopsy specimens on days 3, 7, and 21 were used to assess histological parameters: inflammatory cell infiltration, fibroblast infiltration, collagen deposition, neovascularization, and epithelial thickness, using a semi-quantitative scoring system. Histological analyses were conducted blindly, and statistical analyses were performed using the Kruskal-Wallis test (p< 0.05). Results: On day 3, group A showed significantly higher inflammatory cell infiltration and collagen deposition than other groups, indicating extracellular matrix formation. By day 7, angiogenesis was highest in group A, followed by group B and controls. By day 21, all wounds had completely healed. Epithelial layer thickness, reflecting inflammation and fibroblast maturity, was significantly higher in group A. Conclusion This study compared HA-based hydrogel and hydrocolloid-based dressings through histological analyses to elucidate wound healing mechanics. HA-based hydrogel dressings significantly enhanced wound recovery. However, generalizing these outcomes requires future studies to expand the range of effective wound treatment materials. These findings underscore the potential of HA-based dressings to enhance clinical outcomes in wound management, suggesting avenues for improving therapeutic strategies.

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 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 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 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: Adrenocortical carcinomas (ACCs) are rare tumors with aggressive but varied prognosis. Stage, Grade, Resection status, Age, Symptoms (S-GRAS) score, based on clinical and pathological factors, was found to best stratify the prognosis of European ACC patients. This study assessed the prognostic performance of modified S-GRAS (mS-GRAS) scores including modified grade (mG) by integrating mitotic counts into the Ki67 index (original grade), in Korean ACC patients. Methods: Patients who underwent surgery for ACC between January 1996 and December 2022 at three medical centers in Korea were retrospectively analyzed. mS-GRAS scores were calculated based on tumor stage, mG (Ki67 index or mitotic counts), resection status, age, and symptoms. Patients were divided into four groups (0–1, 2–3, 4–5, and 6–9 points) based on total mS-GRAS score. The associations of each variable and mS-GRAS score with recurrence and survival were evaluated using Cox regression analysis, Harrell’s concordance index (C-index), and the Kaplan–Meier method. Results: Data on mS-GRAS components were available for 114 of the 153 patients who underwent surgery for ACC. These 114 patients had recurrence and death rates of 61.4% and 48.2%, respectively. mS-GRAS score was a significantly better predictor of recurrence (C-index=0.829) and death (C-index=0.747) than each component (P<0.05), except for resection status. mS-GRAS scores correlated with shorter progression-free survival (P=8.34E-24) and overall survival (P=2.72E-13). Conclusion mS-GRAS scores showed better prognostic performance than tumor stage and grade in Asian patients who underwent surgery for ACC.