Femoral nerve injury is a rare but devastating complication of direct anterior approach total hip arthroplasty that occurs in about 1% of the cases and could potentially lead to debilitating loss of knee extension. In this case report, we present a case of femoral nerve injury following direct anterior approach hip arthroplasty with an inability to extend the affected knee, gait instability, and multiple falls. For this patient, an innovative functional adductor magnus muscle transfer was performed to restore knee extension. At 6 months after surgery, the patient\'s knee extension was partly restored, and ambulation was significantly improved.

Apart from listening to the cry of a healthy newborn, it is the declaration by the attending paediatrician in the labour room that the child is normal which brings utmost joy to parents. The global incidence of children born with congenital anomalies has been reported to be 3%-6% with more than 90% of these occurring in low- and middle-income group countries. The exact percentages/total numbers of children requiring surgical treatment cannot be estimated for several reasons. These children are operated under several surgical disciplines, viz, paediatric-, plastic reconstructive, neuro-, cardiothoracic-, orthopaedic surgery etc. These conditions may be life-threatening, e.g., trachea-oesophageal fistula, critical pulmonary stenosis, etc. and require immediate surgical intervention. Some, e.g., hydrocephalus, may need intervention as soon as the patient is fit for surgery. Some, e.g., patent ductus arteriosus need \'wait and watch\' policy up to a certain age in the hope of spontaneous recovery. Another extremely important category is that of patients where the operative intervention is done based on their age. Almost all the congenital anomalies coming under care of a plastic surgeon are operated as elective surgery (many as multiple stages of correction) at appropriate ages. There are advantages and disadvantages of intervention at different ages. In this article, we present a review of optimal timings, along with reasoning, for surgery of many of the common congenital anomalies which are treated by plastic surgeons. Obstetricians, paediatricians and general practitioners/family physicians, who most often are the first ones to come across such children, must know to guide the parents appropriately and convincingly impress upon the them as to why their child should not be operated immediately and also the consequences of too soon or too late.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI\'s ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model\'s performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons\' cognitive loads and improving patients\' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI\'s ChatGPT-4 and Google\'s Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models\' response time. We compared the performance using the Mann-Whitney U test and Student\'s t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4\'s (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini\'s average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT\'-4\'s (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

UNASSIGNED: The advent of 3D printing has revolutionized plastic surgery and prosthetic devices, providing personalized solutions for patients with traumatic injuries, deformities, and appearance-related conditions. This review offers a comprehensive overview of 3D printing\'s applications, advantages, limitations, and future prospects in these fields.
UNASSIGNED: A literature search was conducted in PubMed, Google Scholar, and Scopus for studies on 3D printing in plastic surgery.
UNASSIGNED: 3D printing has significantly contributed to personalized medical interventions, with benefits like enhanced design flexibility, reduced production time, and improved patient outcomes. Using computer-aided design (CAD) software, precise models tailored to a patient\'s anatomy can be created, ensuring better fit, functionality, and comfort. 3D printing allows for intricate geometries, leading to improved aesthetic outcomes and patient-specific prosthetic limbs and orthoses. The historical development of 3D printing, key milestones, and breakthroughs are highlighted. Recent progress in bioprinting and tissue engineering shows promising applications in regenerative medicine and transplantation. The integration of AI and automation with 3D printing enhances surgical planning and outcomes. Emerging trends in patient-specific treatment planning and precision medicine are potential game-changers. However, challenges like technical considerations, economic implications, and ethical issues exist. Addressing these challenges and advancing research in materials, design processes, and long-term outcomes are crucial for widespread adoption.
UNASSIGNED: The review underscores the increasing adoption of 3D printing in healthcare and its impact on plastic surgery and prosthetic devices. It emphasizes the importance of evaluating the current state and addressing knowledge gaps through future research to foster further advancements.

BACKGROUND: Every year, German-speaking experts in plastic, reconstructive, and aesthetic surgery gather to discuss the latest developments at Germany\'s largest conference for plastic surgery, the joint annual meeting of the German Society of Plastic, Reconstructive and Aesthetic Surgery (DGPRÄC) and the Association of German Aesthetic Plastic Surgeons (VDÄPC). Since the topics of the conference have a lasting impact on the practice and research of plastic surgery, an examination of the presented content provides insight into the driving developments in plastic surgery in Germany.
METHODS: We conducted a retrospective network analysis of all abstract titles from the DGPRÄC and VDÄPC annual meeting in 2023. Data were extracted regarding titles, language, author, and place of origin, and the titles were categorized into the four pillars of the specialty. The titles were standardized and subjected to network analysis.
RESULTS: A total of 299 titles from 281 lectures and 18 instructional courses were analyzed. After preprocessing the data, 2463 words with 9384 connections qualified for network analysis. The most frequently mentioned keywords throughout the congress were \'Surgery\', \'Breast\', \'Reconstruction\', \'Flap\', \'Patient\', \'Tissue\', and \'Therapy\'. Locations contributing the highest number of abstracts were Ludwigshafen, Hanover, Leipzig, and Munich.
CONCLUSIONS: In the era of big data, network analysis provides the ability to identify underlying structures and nodes in multidimensional, complex datasets. This study demonstrates the useful application of network analysis to identify thematic focuses and connections at the current DGPRÄC and VDÄPC annual meeting. Sites of intensified research could thus be identified.

BACKGROUND: As one of the most common aesthetic surgical procedures carried out today, blepharoplasty should be in the repertoire of every plastic surgeon. The term blepharoplasty encompasses a wide range of techniques and options that must be tailored to the specific defect and patient one has to treat. A sound knowledge of the upper and lower eyelids\' anatomy is essential for proper surgical execution. Trends have shifted towards more conservative methods (especially of the fat compartment) and sometimes in combination with augmentation techniques, helping to reach a rejuvenated appearance.
OBJECTIVE: To present an overview of the surgical techniques considered for upper lid blepharoplasty and fat pad management, in addition to information on how a surgeon may approach the best treatment for his patient based on current publications in literature.
METHODS: We searched the literature published between 2013, to 2023 using Medline and Reference Citation Analysis. The database was searched using the keywords \"upper blepharoplasty\" AND \"fat\". Papers without full text/abstracts and reviews were excluded. The search strategy followed the PRISMA. The American Society of Plastic Surgeons guidelines for Therapeutic Studies checklist was used to assess all articles. Two authors individually reviewed each article and rated them for importance and relevance to the topic. A consensus was sought and the most relevant studies.
RESULTS: After the application of the selection criteria used in our review, 13 publications were found to address upper lid blepharoplasty specifically. Three of these studies were reviews and three were retrospective studies. Five publications were comparative studies and a further two were clinical trials.
CONCLUSIONS: The tendency of modern surgery is to be conservative, by removing adipose tissue only if strictly necessary and restoring the volume of the upper eyelid in a concept of beauty that espouses a \"full\" sight. There is no gold standard technique to achieve younger and enhanced eyelids. Long-term prospective comparative studies are fundamental in understanding which path is the best to follow.

UNASSIGNED: To avoid the oncologic risks of ipsilateral regional flaps, this study aimed to explore the feasibility and clinical outcomes of the contralateral-based facial artery myomucosal island flap (C-FAMMIF) for oral T2-T3 oncologic defects reconstruction.
UNASSIGNED: A study of flap anatomy was conducted on 7 cadaver samples and a cohort of 24 patients who received C-FAMMIF reconstruction after malignancy resection were retrospectively researched. A balanced anterolateral thigh flap (ALT) group of 47 patients was extracted as control group using propensity score matching method. Progression-free survival (PFS), functional outcomes, and donor site complications were assessed.
UNASSIGNED: Consistent blood supply and drainage through facial artery and vein with median maximum pedicle length of 106 mm supported contralateral reconstruction. The superficial vein drainage pattern indicated safer flap harvest at contralateral neck under circumstances of ipsilateral neck dissections. The pedicle and marginal facial nerve formed three anatomical patterns. The surgical management of each was described. Patients with ipsilateral pN+ neck accounted for 41.7% and 40.4% in the C-FAMMIF and ALT group, respectively. The 2-year PFS rate between the C-FAMMIF and ALT groups was not significantly different (88.2% in C-FAMMIF group and 84.6% in ALT group, respectively, p = 0.6358). Promising recoveries were observed for swallowing function and tactile sensation. The donor sites healed upon primary closure without trismus or permanent facial palsy.
UNASSIGNED: Our findings suggested that C-FAMMIF is feasible and safe for T2-T3 oral oncologic defect reconstruction in patients with ipsilateral cN+ neck.

Since their release, the medical community has been actively exploring large language models\' (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models\' responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) (p = 0.849), while Gemini provided significantly more readable (p = 0.001) and understandable responses (p = 0.014; p = 0.001). There was no difference in the actionability of the models\' responses (p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.

Determining the fundamental characteristics that define a face as \"feminine\" or \"masculine\" has long fascinated anatomists and plastic surgeons, particularly those involved in aesthetic and gender-affirming surgery. Previous studies in this area have relied on manual measurements, comparative anatomy, and heuristic landmark-based feature extraction. In this study, we collected retrospectively at Cedars Sinai Medical Center (CSMC) a dataset of 98 skull samples, which is the first dataset of this kind of 3D medical imaging. We then evaluated the accuracy of multiple deep learning neural network architectures on sex classification with this dataset. Specifically, we evaluated methods representing three different 3D data modeling approaches: Resnet3D, PointNet++, and MeshNet. Despite the limited number of imaging samples, our testing results show that all three approaches achieve AUC scores above 0.9 after convergence. PointNet++ exhibits the highest accuracy, while MeshNet has the lowest. Our findings suggest that accuracy is not solely dependent on the sparsity of data representation but also on the architecture design, with MeshNet\'s lower accuracy likely due to the lack of a hierarchical structure for progressive data abstraction. Furthermore, we studied a problem related to sex determination, which is the analysis of the various morphological features that affect sex classification. We proposed and developed a new method based on morphological gradients to visualize features that influence model decision making. The method based on morphological gradients is an alternative to the standard saliency map, and the new method provides better visualization of feature importance. Our study is the first to develop and evaluate deep learning models for analyzing 3D facial skull images to identify imaging feature differences between individuals assigned male or female at birth. These findings may be useful for planning and evaluating craniofacial surgery, particularly gender-affirming procedures, such as facial feminization surgery.