Search Results (209)

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems. Full article

Healthcare abuse is a critical human rights and public health issue, particularly impacting medical interns and trainees who are vulnerable to mistreatment during their formative professional years. This cross-sectional study, conducted from February to June 2024, evaluated the prevalence and psychological impact of harassment and discrimination among 463 healthcare interns in Saudi Arabia from various specialties, including medicine, nursing, pharmacy, and dentistry. Using a self-administered online questionnaire, we found that mistreatment was widely reported, with female interns experiencing significantly higher rates of sexual harassment and gender-based discrimination. Common perpetrators included residents, lecturers, professors, nurses, and patients, with incidents most frequently occurring in surgical and internal medicine departments. Despite high prevalence, only 9% of interns reported the abuse due to mistrust in reporting systems or failure to recognize the behavior as abuse. These experiences were associated with significant psychological distress, including frustration, reduced motivation to learn, and higher DASS scores, particularly among female interns. The study underscores the need for institutional reforms, including policy development, cultural change, and effective reporting systems to ensure a safe and supportive learning environment for future healthcare professionals. Addressing abuse in medical training is essential for individual well-being and the sustainability and integrity of healthcare systems. Full article

Background: It is crucial to predict the response to chemotherapy and identify prognostic markers for recurrence and survival in patients with epithelial ovarian cancer (EOC), in order to effectively manage patient care. The CA-125 elimination rate constant K (KELIM) has recently been developed as a means of assessing the chemotherapy response and has been tested mainly in patients enrolled in randomized controlled trials. The objective of this study was to investigate whether the KELIM score is a prognostic marker for progression-free survival (PFS) and overall survival (OS) in EOC, utilizing its role in predicting the chemotherapy response in real-life settings. Method: Demographic, surgical, and survival data of patients with EOC operated on in Antalya Training and Research Hospital between January 2015 and December 2021 were obtained from the electronic gynecological oncology clinic database system and analyzed retrospectively. Results: A total of 102 patients with EOC were included; 30 patients (29.4%) had a KELIM score ≥ 1 and 72 (70.6%) patients had a KELIM score < 1. In the group with a KELIM score < 1, recurrence and refractory disease occurred in 49 patients, while it was 11 patients in the group with a KELIM score ≥ 1 (p = 0.004). PFS was 12 months and 32 months in the groups with KELIM scores of <1 and ≥1, respectively (p = 0.012). There was no difference between groups regarding OS (p = 0.139). In the whole group, KELIM score (<1 vs. ≥1) and type of surgery (IDS vs. PDS) were found to be independent prognostic factors for PFS (RR = 0.44; 95%CI: 0.22–0.88; p = 0.021 and RR = 2.97; 95%CI: 1.76–5.01; p < 0.001, respectively). Conclusion: We found that a favorable KELIM score was associated with better PFS in all groups of patients undergoing surgery for EOC in a real-life setting. With the increasing number of studies, the KELIM score will play an important role in providing better guidance to clinicians at the initial presentation of patients and in subsequent treatment planning. Full article

This paper presents a Multimodal Hidden Markov Model (MHMM) framework specifically designed for real-time human proficiency assessment, integrating physiological (Heart Rate Variability (HRV)), behavioral (Task Completion Time (TCT)), and subjective (NASA Task Load Index (NASA-TLX)) data streams to infer latent human proficiency states in industrial settings. Using published empirical data from the surgical training literature, a comprehensive simulation study was conducted, with the MHMM (Trained) achieving 92.5% classification accuracy, significantly outperforming unimodal Hidden Markov Model (HMM) variants 61–63.9% and demonstrating competitive performance with advanced models such as Long Short-Term Memory (LSTM) networks 90%, and Conditional Random Field (CRF) 88.5%. The framework exhibited robustness across stress-test scenarios, including sensor noise, missing data, and imbalanced class distributions. A key advantage of the MHMM over black-box approaches is its interpretability by providing quantifiable transition probabilities that reveal learning rates, forgetting patterns, and contextual influences on proficiency dynamics. The model successfully captures context-dependent effects, including task complexity and cumulative fatigue, through dynamic transition matrices. When demonstrated through simulation, this framework establishes a foundation for developing adaptive operator-AI collaboration systems in Industry 5.0 environments. The MHMM’s combination of high accuracy, robustness, and interpretability makes it a promising candidate for future empirical validation in real-world industrial, healthcare, and training applications in which it is critical to understand and support human proficiency development. Full article

Background/Objectives: The twin-to twin transfusion syndrome (TTTS) is the most common complication of monochorionic twin pregnancies. Fetal laser therapy (FLT) and serial amniondrainage (SAD) have been used as treatment options for TTTS. This study examines how the management of TTTS in Germany has evolved in the past years and addresses future patient needs and potential challenges for healthcare providers and healthcare systems. Methods: The number of TTTS-related interventions between the years 2005 and 2021 were extracted from the German Federal Statistical Office. The trajectory of FLT and SAD procedures over the study period was analyzed. The historical data were used to make projections for future years and address future FLT surgical needs. Further, we aimed to determine age-related influences in monochorionic twin pregnancies requiring FLT. Results: A statistically significant increase in the number of FLT surgeries and a noteworthy decline in the number of SAD procedures with respect to both the number of deliveries per year and the number of multiple pregnancies per year were noted. For the first time, we showed that the percentage of multiple pregnancies requiring FLT was significantly higher in younger mothers under 25 years of age, than in all other age groups. Conclusions: For the moment, FLT poses the only direct and causative treatment of TTTS. The results of our analysis reveal an increasing demand for FLT surgeries for future years. We highlight the need to train more maternal–fetal medicine specialists to be able to perform the procedure safely and to allocate resources efficiently to accommodate the rising number of cases. Full article

Craniofacial deformities, particularly craniosynostosis, present significant surgical challenges due to complex anatomy and the need for individualised, high-precision interventions. Augmented reality (AR) has emerged as a promising tool in craniofacial surgery, offering enhanced spatial visualisation, real-time anatomical referencing, and improved surgical accuracy. This review explores the current and emerging applications of AR in preoperative planning, intraoperative navigation, and surgical education within paediatric craniofacial surgery. Through a literature review of peer-reviewed studies, we examine how AR platforms, such as the VOSTARS system and Microsoft HoloLens, facilitate virtual simulations, precise osteotomies, and collaborative remote guidance. Despite demonstrated benefits in feasibility and accuracy, widespread clinical adoption is limited by technical, ergonomic, financial, and training-related challenges. Future directions include the integration of artificial intelligence, haptic feedback, and robotic assistance to further augment surgical precision and training efficacy. AR holds transformative potential for improving outcomes and efficiency in craniofacial deformity correction, warranting continued research and clinical validation. Full article

Objectives: This review summarizes current evidence on robotic-assisted upper airway and neck surgery in pediatric patients, highlighting clinical indications, outcomes, limitations, and areas for future research. Methods: A systematic review was conducted in accordance with PRISMA guidelines, including studies on robotic surgery for pediatric patients (≤18 years) with upper airway conditions and cervical pathologies. Data on study characteristics, patient demographics, surgical details, outcomes, and robotic system advantages or limitations were extracted. Results: Twenty studies met inclusion criteria, comprising 104 pediatric patients who underwent 110 robotic procedures, mostly transoral robotic surgery (TORS) for base of tongue, laryngeal, and cervical pathologies. The Da Vinci Si was the most used system. The mean operative time was ~74 min, with minimal blood loss and no intra/post operative tracheostomies. Reported advantages included enhanced visualization, precision, and reduced morbidity. Limitations involved size mismatches, limited working space, and high costs. Follow-up (mean 11.4 months) revealed no recurrences, confirming feasibility and safety in selected pediatric cases. Conclusions: Robotic-assisted surgery appears to be a feasible and safe option for managing pediatric upper airway and neck conditions, offering promising functional and aesthetic outcomes with low complication rates. However, its use is currently limited by anatomical constraints, high costs, and the need for surgeon training. Long-term prospective studies with larger cohorts are needed to confirm its efficacy and define its role compared to traditional techniques. Full article

Background: Surgery remains the cornerstone of diagnosis and treatment for gastric cancer. This study aims to develop and validate a serology-based clinical scoring system to predict and evaluate the prognosis of gastric cancer patients. Methods: Clinicopathological data of primary gastric cancer patients who underwent surgical treatment from 2009 to 2018 were collected and divided into training and validation cohorts. Preoperative serological indicators were screened, and a serum risk score (SerScore) was developed using LASSO-Cox analysis. Prognosis prediction models incorporating the SerScore were established and validated. Results: A total of 5493 patients were screened, and 43 serological indicators were assessed. Twelve serological indicators were selected to construct the SerScore. Patients with a SerScore below the cut-off value of −1.73 had significantly better survival rates compared to those with higher scores. Multivariate Cox analysis identified SerScore, age, tumor location, T stage, and N stage as independent prognostic factors for overall survival in the training cohort. A multivariate nomogram was developed, achieving a C-index of 0.745 in the training cohort and 0.750 in the validation cohort. The nomogram demonstrated superior predictive accuracy compared to the SerScore alone, with AUC values of 0.783 versus 0.639 in the training cohort and 0.805 versus 0.657 in the validation cohort. Calibration curves closely aligned with ideal predictions in both cohorts. Conclusions: The SerScore model provides an effective tool for prognostic assessment in primary gastric cancer patients. This model not only enhances prognostic evaluation but also establishes a foundation for developing advanced prediction tools for gastric cancer. Full article

Clinical text classification presents significant challenges in healthcare informatics due to inherent asymmetries in domain-specific terminology, knowledge distribution across specialties, and imbalanced data availability. We introduce MTTL-ClinicalBERT, a symmetrical multi-task transfer learning framework that harmonizes knowledge sharing across diverse medical specialties while maintaining balanced performance. Our approach addresses the fundamental problem of symmetry in knowledge transfer through three innovative components: (1) an adaptive knowledge distillation mechanism that creates symmetrical information flow between related medical domains while preventing negative transfer; (2) a bidirectional hierarchical attention architecture that establishes symmetry between local terminology analysis and global contextual understanding; and (3) a dynamic task-weighting strategy that maintains equilibrium in the learning process across asymmetrically distributed medical specialties. Extensive experiments on the MTSamples dataset demonstrate that our symmetrical approach consistently outperforms asymmetric baselines, achieving average improvements of 7.2% in accuracy and 6.8% in F1-score across five major specialties. The framework’s knowledge transfer patterns reveal a symmetric similarity matrix between specialties, with strongest bidirectional connections between cardiovascular/pulmonary and surgical domains (similarity score 0.83). Our model demonstrates remarkable stability and balance in low-resource scenarios, maintaining over 85% classification accuracy with only 30% of training data. The proposed framework not only advances clinical text classification through its symmetrical design but also provides valuable insights into balanced information sharing between different medical domains, with broader implications for symmetrical knowledge transfer in multi-domain machine learning systems. Full article

Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC). Methods: Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision. Results: This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system’s ability to reliably detect tumor regions and showing high concordance with histopathological findings. Conclusion: The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation. Full article

Introduction: Mastering microsurgery requires advanced fine motor skills, hand–eye coordination, and precision, making it challenging for novices. Robot-assisted microsurgery offers benefits, such as eliminating physiological tremors and enhancing precision through motion scaling, which may potentially make learning microsurgical skills easier. Materials and Methods: Sixteen medical students without prior microsurgical experience performed 160 anastomoses in a synthetic model. The students were randomly assigned into two cohorts, one starting with the conventional technique (HR group) and one with robotic assistance (RH group) using the Symani surgical system. Results: Both cohorts showed a reduction in procedural time and improvement in SAMS scores over successive attempts, with robotic anastomoses demonstrating a 48.2% decrease in time and a 54.6% increase in SAMS scores. The decreases were significantly larger than the RH group (p < 0.05). The quality of the final anastomoses was comparable in both groups (p > 0.05). Discussion: This study demonstrated a steep preclinical learning curve for robot-assisted microsurgery (RAMS) among novices in a synthetic, preclinical model. No significant differences in SAMS scores between robotic and manual techniques after ten anastomoses. Robot-assisted microsurgery required more time per anastomosis, but the results suggest that experience with RAMS may aid in manual skill acquisition. The study indicates that further exploration into the sequencing of robotic and manual training could be valuable, especially in designing structured microsurgical curricula. Full article

Background: High-voltage injuries associated with train surfing are a distinct subset of electrical injuries, yet detailed analyses remain limited. This study retrospectively reviewed train-surfing injuries admitted between 1994 and 2024, comparing their characteristics and outcomes to work-related high-voltage injuries. Methods: Medical records of 102 patients admitted for high-voltage injuries were analyzed, including 32 train-surfing and 70 work-related cases. Demographics, injury patterns, and clinical outcomes were assessed. Results: Train surfers were predominantly young males (median age 19 years), while work-related injuries involved slightly older males (median age 34 years). Train surfers sustained more severe burns (%TBSA: 47.6% vs. 25.4%, p < 0.0001) and higher ABSI scores (6.7 vs. 5.3, p < 0.01). Vertical electrical flow was predominant in train surfing (65.6%), reflecting contact with overhead lines, while work-related injuries showed varied flow patterns, with diagonal flow being most frequent (58.6%). Train surfers had longer ICU stays (38.7 vs. 17.9 days, p < 0.001) and underwent more surgeries per patient (5.3 vs. 2.8, p < 0.01). Fasciotomy rates were significantly higher among train surfers (84.4% vs. 55.7%, p < 0.01), as were amputations (53.1% vs. 25.7%, p < 0.001). Mortality rates were similar in both groups (25%). Conclusions: Train-surfing injuries represent a distinct and highly severe subgroup of high-voltage trauma, marked by greater burn extent, predominantly vertical electrical flow due to contact with overhead lines, and significantly higher surgical complexity—including increased rates of fasciotomies and amputations. Despite comparable mortality, the clinical burden for train-surfing victims is substantially higher, reflected in longer ICU stays and more operations per patient. These findings underscore the urgent need for targeted prevention strategies addressing youth engagement in train surfing. Public health campaigns, railway infrastructure modifications (e.g., deterrent systems or physical barriers), and early educational interventions could play a critical role in reducing these preventable injuries. Furthermore, trauma centers should be prepared for the specific reconstructive and critical care demands posed by this high-risk group, emphasizing the importance of specialized multidisciplinary management protocols. Full article

Background: Surgical residents are a high-risk population for burnout, yet no studies have assessed its prevalence among thoracic surgery residents in Europe or Italy. Methods: A nationwide cross-sectional survey was conducted among Italian thoracic surgery residents to assess burnout and quality of life. The Maslach Burnout Inventory measured burnout risk, while tailored questions evaluated quality of life. Univariate and multivariable analyses identified burnout risk factors, and χ² tests explored relevant associations between variables. Results: Of 193 eligible residents, 98 (50.8%) completed the survey. High burnout risk was identified in 60.2% of respondents. Independent risk factor associations between burnout risk and low perceived inclusion and aggregation, low colleague quality, low residency program rating, low personal life satisfaction, perceived lack of valorization, and exposure to sexual harassment were not significant in multivariable models. No differences in burnout risk were found across gender, geographic location, or training year. Conclusions: Burnout among Italian thoracic surgery residents underscores systemic challenges such as excessive administrative demands, insufficient mentorship, limitations to self-care, and gaps in theoretical training. Addressing these issues requires comprehensive reforms, including curriculum enhancement, strengthened mentorship, improved administrative support, and accessible mental health resources. A multi-level intervention strategy is essential to enhance resident well-being and training quality. Full article

Background/Objectives: Groin hernia repair is a common surgical procedure worldwide, with increasing adoption of minimally invasive techniques. However, the adoption of laparoscopic repair varies significantly across healthcare systems. This study aims to analyze trends in laparoscopic versus open-groin hernia repair in Romania over a five-year period (2019–2023), assessing differences in hospital types, reimbursement policies, and patient outcomes. Methods: This nationwide retrospective study examined 76,553 groin hernia repairs from the National Diagnosis-Related Group (DRG) database, including 231 public and 41 private hospitals. Patients were categorized as laparoscopic (13,282 cases) or open repair (63,271 cases). Statistical analysis included logistic regression and non-parametric tests to assess factors influencing surgical approach selection, hospitalization duration, and case complexity. Results: Laparoscopic repair accounted for 17.3% of all groin hernia procedures, with higher adoption in private hospitals (54.7%) than in public hospitals (14.6%). Laparoscopic procedures increased from 14.1% in 2019 to 20% in 2023. Hospitalization was shorter in private hospitals (1.78 vs. 4.80 days in public hospitals). Reimbursement rates showed minimal differentiation between laparoscopic and open repair, suggesting no financial incentive for minimally invasive surgery in public hospitals. Conclusions: Despite a steady increase in laparoscopic hernia repair, its adoption in Romania remains limited compared to Western Europe. Private hospitals lead in minimally invasive surgery, while public hospitals predominantly rely on open repair due to reimbursement policies and resource constraints. Adjusting DRG-based reimbursement, expanding training, and implementing a national hernia registry could improve outcomes and access to minimally invasive surgery. Full article

Background: Minimally invasive surgery (MIS) is an advanced surgical technique that relies on a camera to provide the surgeon with a visual field. When the camera rotates along its longitudinal axis, the horizon of the surgical view tilts, increasing the difficulty of the procedure and the cognitive load on the surgeon. To address this, we proposed training a convolutional neural network (CNN) to detect camera rotation, laying the groundwork for the automatic correction of this issue during MIS procedures. Methods: We collected trans-nasal MIS procedure videos from YouTube and labeled each frame as either “tilted” or “non-tilted”. The dataset consisted of 2116 video frames, with 497 frames labeled as “tilted” and 1619 frames as “non-tilted”. This dataset was randomly divided into three subsets: training (70%), validation (20%), and testing (10%) Results: The ResNet50 was trained on the dataset for 10 epochs, achieving an accuracy of 96.9% at epoch 6 with a validation loss of 0.0242 before validation accuracy began to decrease. On the test set, the model achieved an accuracy of 96% with an average loss of 0.0256. The final F1 score was 0.94, and the Matthews Correlation Coefficient was 0.9168, with no significant bias toward either class. The trained ResNet50 model demonstrated a high success rate in predicting significant camera rotation without favoring the more frequent class in the dataset. Conclusions: The trained CNN accurately detected camera rotation with high precision, establishing a foundation for developing an automatic correction system for camera rotation in MIS procedures. Full article