Samet Engin Ucar

Background: Laparoscopic living‐donor nephrectomy improves graft function and reduces morbidity. Intraoperative complications in donor nephrectomy are rare and can be avoided by basic precautions. We aimed to investigate intraoperative surgical complications through a video presentation and to emphasize protective measures. Methods: This is a retrospective cohort study. We included patients who underwent donor nephrectomy between December 2015 and December 2025. All patients underwent laparoscopic transperitoneal donor nephrectomy. We obtained donors' medical data from the hospital system. We identified intraoperative complications recorded in the operation notes and obtained operation videos of complicated cases from hospital archives. Results: Our center performed 1011 living‐donor nephrectomies. There were 816 laparoscopic left nephrectomies and 195 laparoscopic right nephrectomies. The mean postoperative hospitalization time for donors was 2.8 days (min‐max: 2–5 days). The mean donor BMI was 28.35 (min‐max: 19.33–41.73). We observed 68 (6.7%) complications. Videos of the complications are presented. One case required conversion to open surgery. Conclusion: Laparoscopic donor nephrectomy is an advanced surgery with low complication risks. Experienced surgeons can manage complications laparoscopically.

Background: Artificial intelligence (AI) has rapidly emerged as a major research domain in orthopaedics, driven by advances in computational methods and the widespread availability of digital clinical data. Although AI-related orthopaedic publications have increased substantially, the extent to which this growth reflects a balanced distribution of research activity across clinical subspecialties and AI modalities remains unclear. Purpose: The aim of this study was to evaluate temporal trends, subspecialty distribution, and methodological characteristics of AI-related orthopaedic research activity using a bibliometric approach. Methods: A bibliometric analysis was performed using publications indexed in two major scientific databases between 2016 and 2025. AI-related orthopaedic articles were identified using predefined search terms. Publications were analyzed according to annual publication trends, orthopaedic subspecialty classification, AI methodology, and proxy indicators of study design and evidence development. Subspecialty assignment was performed using a standardized taxonomy, and descriptive analyses were used to characterize research patterns. Results: AI-related orthopaedic publications increased exponentially during the study period. Research output was unevenly distributed across subspecialties, with adult reconstruction and spine surgery accounting for the largest share of publications, whereas foot and ankle surgery, pediatric orthopaedics, hand & wrist, and shoulder & elbow surgery remained underrepresented. Deep learning–based imaging studies predominated, while studies incorporating structured clinical data, longitudinal outcomes, and external validation were comparatively limited. Much of the literature remains exploratory, primarily focusing on early-phase model development. Conclusions: AI research in orthopaedics has expanded rapidly but remains methodologically heterogeneous, with research activity heavily concentrated in data-rich subspecialties such as adult reconstruction and spine surgery. The predominance of early-phase studies indicates that current publication volumes are largely driven by data accessibility rather than a balanced distribution across all orthopaedic domains. Future research should encourage multicenter collaboration, standardized reporting, and targeted studies in underrepresented subspecialties to ensure a more balanced development of orthopaedic AI tools.