Search Results (158)

Posterior capsule opacification (PCO), a frequent complication of cataract surgery, arises from dysregulated wound healing and fibrotic transformation of residual lens epithelial cells. While transcriptomic and machine learning (ML) approaches have elucidated fibrosis-related pathways in other tissues, the molecular divergence between regenerative and fibrotic outcomes in the lens remains unclear. Here, we used an ex vivo chick lens injury model to simulate post-surgical conditions, collecting RNA from lenses undergoing either regenerative wound healing or fibrosis between days 1–3 post-injury. Bulk RNA sequencing data were normalized, log-transformed, and subjected to univariate filtering prior to training LASSO, SVM, and RF ML models to identify discriminatory gene signatures. Each model was independently validated using a held-out test set. Distinct gene sets were identified, including fibrosis-associated genes (VGLL3, CEBPD, MXRA7, LMNA, gga-miR-143, RF00072) and wound-healing-associated genes (HS3ST2, ID1), with several achieving perfect classification. Gene Set Enrichment Analysis revealed divergent pathway activation, including extracellular matrix remodeling, DNA replication, and spliceosome associated with fibrosis. RT-PCR in independent explants confirmed key differential expression levels. These findings demonstrate the utility of supervised ML for discovering lens-specific fibrotic and regenerative gene features and nominate biomarkers for targeted intervention to mitigate PCO. Full article

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

(1) Medical training is changing, especially for surgeons. Virtual reality simulation is an excellent way to train surgeons safely. Studies show that surgeons who train with simulation have demonstrated improved technical skills in fundamental surgical procedures. The purpose of this study is to determine the overall impact of virtual reality training on laparoscopic cholecystectomy performance and to explore whether specific training protocols or the addition of feedback confer any advantages for future surgeons. (2) MEDLINE (PubMed), Embase (Ovid SP), Web of Science, Google Scholar, and Scopus were searched for the literature related to virtual reality training, immersive simulation, laparoscopic surgical skills training, and medical education. Study quality was assessed using the Cochrane Risk of Bias Tool and NIH Quality Assessment Tool. (3) A total of 55 full-text articles were reviewed. Meta-analysis showed that virtual reality training is an effective method for learning cholecystectomy surgical skills. (4) Conclusions: Performance, measured by objective structured assessments and time to task completion, is improved with virtual reality training compared with no additional training. Positive effects of simulation training were evident in global rating scores and operative time. Continuous feedback on movement parameters during laparoscopic cholecystectomy skills training impacts skills acquisition and long-term retention. Full article

Resistive stretching sensors are currently used in healthcare robotics due to their ability to vary electrical resistance when subjected to mechanical strain. However, commercial sensors often lack the softness required for integration into soft structures. This study presents a detailed methodology to characterize fabric-based resistive stretching sensors, focusing on both static and dynamic performance, for application in a smart vascular simulator for surgical training. Five sensors, called #1–#5, were developed using conductive fabrics integrated into soft silicone. Stability and fatigue tests were performed to evaluate their behavior. The surface structure and fiber distribution were analyzed using digital microscopy and scanning electron microscopy, while element analysis was performed via Energy-Dispersive X-ray Spectroscopy. Sensors #1 and #3 are the most stable with a low relative standard deviation and good sensitivity at low strains. Sensor #3 showed the lowest hysteresis, while sensor #1 had the widest operating range (0–30% strain). Although all sensors showed non-monotonic behavior across 0–100% strain, deeper investigation suggested that the sensor response depends on the configuration of conductive paths within and between fabric layers. Soft fabric-based resistive sensors represent a promising technical solution for physical simulators for surgical training. Full article

Background: Minimally invasive surgery (MIS) is increasingly adopted in pediatric surgical practice, yet it demands specific technical skills that require structured training. Simulation-based education offers a safe and effective environment for skill acquisition, especially in complex procedures such as thoracoscopic repair of esophageal atresia with tracheoesophageal fistula (EA-TEF). Objective: This study aimed to evaluate the effectiveness of a 3D-printed simulator for training pediatric surgeons in thoracoscopic EA-TEF repair, assessing improvements in operative time and technical performance. Methods: A high-fidelity, 3D-printed simulator replicating neonatal thoracic anatomy was developed. Six pediatric surgeons at different training levels performed eight simulation sessions, including fistula ligation and esophageal anastomosis. Operative time and technical skill were assessed using the Stanford Microsurgery and Resident Training (SMaRT) Scale. Results: All participants showed significant improvements. The average operative time decreased from 115.6 ± 3.51 to 90 ± 6.55 min for junior trainees and from 100.5 ± 3.55 to 77.5 ± 4.94 min for senior trainees. The mean SMaRT score increased from 23.8 ± 3.18 to 38.3 ± 3.93. These results demonstrate a clear learning curve and enhanced technical performance after repeated sessions. Conclusions: Such 3D-printed simulation models represent an effective tool for pediatric MIS training. Even within a short time frame, repeated practice significantly improves surgical proficiency, supporting their integration into pediatric surgical curricula as an ethical, safe, and efficient educational strategy. 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: Teaching methodology has recently undergone significant evolution from traditional apprenticeship models as we adapt to ever-increasing rates of technological advancement. Big data, artificial intelligence, and machine learning are on the precipice of revolutionising all aspects of surgical practice, with far-reaching implications. Robotic platforms will increase in autonomy as machine learning rapidly becomes more sophisticated, and therefore training requirements will no longer slow innovation. Materials and Methods: A search of published studies discussing surgeon training and computer-enhanced simulation robotics and emerging technologies using MEDLINE, PubMed, EMBASE, Scopus, CRANE, CINAHL, and Web of Science was performed in January 2024. Online resources associated with proprietary technologies related to the subject matter were also utilised. Results: Following a review of 3209 articles, 91 of which were published, relevant articles on aspects of robotics-based computer-enhanced simulation, technologies, and education were included. Publications ranged from RCTs, cohort studies, meta-analysis, and systematic reviews. The content of eight medical technology-based websites was analysed and included in this review to ensure the most up-to-date information was analysed. Discussion: Surgeons should aim to be at the forefront of this revolution for the ultimate benefit of patients. Surgical exposure will no longer be due to incidental experiences. Rather, surgeons and trainees will have access to a complete database of simulated minimally invasive procedures, and procedural simulation certification will likely become a requisite from graduation to live operating to maintain rigorous patient safety standards. This review provides a comprehensive outline of the current and future status of surgical training in the robotic and digital era. 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

Three-dimensional (3D) printing has gained substantial interest among scientists and surgeons over the past decade due to its broad potential in medical applications. Its clinical utility has been increasingly recognized, demonstrating promising outcomes for patient care. Currently, 3D printing technology enables surgeons to enhance operative precision by facilitating the creation of patient-specific anatomical models, customized implants, biological tissues, and even surgical instruments. This personalization contributes to improved surgical outcomes, reduced operative times, and shorter postoperative recovery periods. Furthermore, 3D printing significantly aids in the customization of prostheses to conform closely to individual anatomical structures. Beyond therapeutic applications, 3D printing serves as a valuable educational tool in medical training. It enhances case-specific visualization, elucidates fracture mechanisms, and provides tangible models for simulation-based practice. Although the use of 3D printing might be seen as useful mostly in orthopedics, it has expanded into multiple medical specialties, including plastic surgery, dentistry, and emergency medicine. Presently, 3D-printed constructs are routinely employed for preoperative planning, prosthetic development, fracture management, and the fabrication of patient-specific surgical tools. Futuristically, the integration of 3D printing into clinical practice is expected to play a pivotal role in the advancement of personalized medicine, offering substantial benefits for both healthcare providers and patients. Full article

(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) is enhancing clinical precision, streamlining workflows, and redefining dental education. This review examines the evolution, applications, and future potential of these technologies in modern dental practice. (2) Methods: A narrative literature review was conducted, synthesizing findings from recent studies on digital radiography, CBCT, AI-assisted diagnostics, 3D imaging, and involving simulation tools (AR/VR). Peer-reviewed journal articles, systematic reviews, and clinical studies were analyzed to explore their impact on diagnosis, treatment planning, surgical execution, and training. (3) Results: Digital and 3D imaging modalities have improved diagnostic accuracy and reduced radiation exposure. AI applications enhance image interpretation, automate clinical tasks, and support treatment simulations. AR and VR technologies provide involved, competency-based surgical training and real-time intraoperative guidance. Integrating 3D printing and portable imaging expands accessibility and personalization in care delivery. (4) Conclusions: The integration of CBCT, AI, AR, and VR represents a paradigm shift in dentistry, elevating precision, efficiency, and patient outcomes. Continued research, standardization, and ethical practice will be essential for widespread adoption and maximizing clinical benefits. Full article

Background and Aims: Esophageal atresia (EA) and tracheoesophageal fistula (TEF) are rare but serious congenital anomalies requiring early surgical intervention. Over the past two decades, minimally invasive surgical (MIS) approaches—particularly thoracoscopic repair—have gained traction, aiming to reduce postoperative morbidity while maintaining surgical efficacy. Objective: This narrative review provides a comprehensive overview of the evolution and current status of MIS techniques for EA/TEF, assessing their clinical outcomes, technical challenges, and implications for patient care. Methods: A structured literature search was conducted to identify clinical studies, reviews, and reports on thoracoscopic, robotic-assisted, and endoscopic approaches to EA/TEF. Emerging adjuncts, including tissue engineering, botulinum toxin use, and magnet-assisted anastomosis, were also reviewed. Results: Thoracoscopic repair has demonstrated comparable anastomotic success rates to open surgery (approximately 85–95%) with significantly reduced rates of musculoskeletal complications, such as scoliosis and chest wall deformities (reported in less than 10% of cases, compared to up to 40% in open approaches). Robotic-assisted and endoscopic-assisted techniques have enabled improved visualization and precision in anatomically challenging cases, although their use remains limited to high-resource centers with specialized expertise. Common postoperative complications include anastomotic stricture (30–50%), gastroesophageal reflux disease (35–70%), and respiratory morbidity, necessitating long-term multidisciplinary follow-up. Recent innovations in simulation-based training and bioengineered adjuncts have facilitated safer MIS adoption in neonates. Conclusions: Minimally invasive techniques have improved the surgical management of EA/TEF, though challenges remain regarding technical complexity, training, and resource availability. Continued innovation and collaborative research are essential for advancing care and ensuring optimal outcomes for affected infants. Full article

This study aimed to assess the clinical value of three-dimensional printed personalised vascular models (3DPPVMs) in assisting with the pre-operative planning and simulation of endovascular interventions. CT angiographic images of four cases, namely, abdominal aorta aneurysm (AAA), carotid artery stenosis, coronary artery stenosis, and renal artery stenosis, were selected, and 3DPPVMs were obtained. A total of 21 clinicians specialising in interventional radiology and vascular surgery were invited to participate in the study, comprising 6 radiologists and 15 vascular surgeons. Of these, 66.7% had not used a 3DPPVM prior to their participation. Considering all areas of experience and all four models, it was observed that 75% of the participants gave a ranking of 7 or above out of 10 with regard to the recommendation of the use of the 3DPPVMs. The mean scores of the participants’ ranking of the models ranged from 3.2 to 4.3 out of 5. The AAA model was ranked the highest for realism (4.10 ± 0.89, p = 0.002), the planning of interventions and simulations (3.90 ± 1.12 and 4.05 ± 0.95), the development of haptic skills (3.56 ± 0.98), reducing the procedure time (3.47 ± 1.12), and clarifying the pathology to patients (4.33 ± 0.69, p all >0.05), indicating consistency amongst the participants. The carotid artery model was ranked the highest for accurately displaying anatomical structures (4.3 ± 0.73). All the 3DPPVMs enhanced the understanding of the disease demonstrated, with rankings between 3.8 and 3.95. All the models aided in elucidating the intervention procedure required and in the planning of vascular interventions, with rankings of 3.5 and 3.9. The highest rankings were given by qualified clinicians with 8 or more years of experience. This study shows the potential value of using 3D-printed vascular models in education for clinicians and patients, as well as for clinical training and the pre-surgical simulation of endovascular stent-grafting procedures. Full article

Precise and real-time estimation of the robotic arm’s position on the patient’s side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of $\mathcal{O}(LlogL)$. The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90% under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework’s superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery. Full article

Background: Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. Objective: This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. Methods: A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Results: Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Conclusions: Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance. Full article

Background and Objectives: Realistic surgical simulation models are essential for neurosurgical training, particularly in glioma resection. We developed a patient-specific simulation model designed for fluorescence-guided glioma resection, providing an anatomically accurate and reusable platform for surgical education. While insular gliomas were used as an example, the model can be adapted to simulate gliomas in other brain regions, making it a versatile training tool. Methods: Using open-source 3D software, we created a digitally reconstructed skull, brain, and cerebral vessels, including a fluorescent insular glioma. The model was produced through additive manufacturing and designed with input from neurosurgeons to ensure a realistic and reusable representation of the Sylvian fissure and bone structures. The simulator’s educational effectiveness and usability were evaluated by two senior physicians, four assistant physicians, and six medical students using actual microsurgical instruments. Assessments were based on subjective and objective criteria. Results: Subjective evaluations, using a 5-point Likert scale, showed high face and content validity. Objective measures demonstrated strong construct validity, accurately reflecting the participant’s skills. Medical students and resident neurosurgeons showed marked improvement in their learning curve over three attempts, with progressive improvement in performance. Conclusions: This simulation model addresses advanced neurosurgical training needs by providing a highly realistic, cost- effective, and adaptable platform for fluorescence-guided glioma resection. Its effectiveness in enhancing surgical skills suggests significant potential for broader integration into neurosurgical training programs. Further studies are warranted to explore its applications in different glioma localizations and training settings. Full article