Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.

PURPOSE: Dysphagia is the inability or difficulty to swallow normally. Standard procedures for diagnosing the exact disease are, among others, X-ray videofluoroscopy, manometry and impedance examinations, usually performed consecutively. In order to gain more insights, ongoing research is aiming to collect these different modalities at the same time, with the goal to present them in a joint visualization. One idea to create a combined view is the projection of the manometry and impedance values onto the right location in the X-ray images. This requires to identify the exact sensor locations in the images. METHODS: This work gives an overview of the challenges associated with the sensor detection task and proposes a robust approach to detect the sensors in X-ray image sequences, ultimately allowing to project the manometry and impedance values onto the right location in the images. RESULTS: The developed sensor detection approach is evaluated on a total of 14 sequences from different patients, achieving a F1-score of 86.36%. To demonstrate the robustness of the approach, another study is performed by adding different levels of noise to the images, with the performance of our sensor detection method only slightly decreasing in these scenarios. This robust sensor detection provides the basis to accurately project manometry and impedance values onto the images, allowing to create a multimodal visualization of the swallow process. The resulting visualizations are evaluated qualitatively by domain experts, indicating a great benefit of this proposed fused visualization approach. CONCLUSION: Using our preprocessing and sensor detection method, we show that the sensor detection task can be successfully approached with high accuracy. This allows to create a novel, multimodal visualization of esophageal motility, helping to provide more insights into swallow disorders of patients.

[Modern approaches and technologies to prevent anastomotic leakage in the gastrointestinal tract]. / Moderne Ansätze und Technologien zur Verhinderung der Anastomoseninsuffizienz am Gastrointestinaltrakt.

The healing of gastrointestinal anastomoses is a complex biological process influenced by numerous factors. Various strategies to support healing and prevent anastomotic leakage (AL) exist, encompassing preoperative, intraoperative and postoperative measures. Preoperative interventions aim to optimize the patient and the tissue environment, particularly the gut microbiome. Intraoperative measures are technical in nature and include the choice of surgical access, the anastomotic technique and anastomotic reinforcement. Various procedures of the intraoperative diagnostics enable identification of such anastomoses requiring additional protective measures. Recently, indocyanine green (ICG) fluoroscopy has become established for evaluation of tissue perfusion, while newer techniques such as spectral microscopy offer promising possibilities. Postoperative diagnostic methods aim to identify potential AL as early as possible to enable initiation of therapeutic steps even before the onset of symptoms. These procedures range from various imaging techniques to innovative bioresorbable, pH-sensitive implants for early AL detection. Due to the multifactorial genesis of AL and the diverse technical possibilities, no single method will become established for prevention of AL. Instead, a combination of measures will ultimately lead to optimal anastomotic healing. The use of artificial intelligence and analyses based on the data promises a better interpretation of the vast amount of data and therefore to be able to provide general recommendations.

Role of the serosa in intestinal anastomotic healing: insights from in-depth histological analysis of human and murine anastomoses.

BACKGROUND: Anastomotic leakage following colorectal surgery remains a significant complication despite advances in surgical techniques. Recent findings on serosal injury repair in coelomic cavities, such as the peritoneum, challenge the current understanding of the cellular origins and mechanisms underlying intestinal anastomotic healing. Understanding the contribution of each layer of the intestinal wall during anastomotic healing is needed to find new therapeutic strategies to prevent anastomotic leakage. The aim of this experimental study was to investigate the role of the serosal layer of the intestinal wall in anastomotic healing. MATERIALS AND METHODS: Comprehensive histologic analysis of human and murine anastomoses was performed to elucidate histologic changes in the different intestinal layers during anastomotic healing. In vivo staining of the extracellular matrix (ECM) in the serosal layer was performed using a fluorophore-conjugated N-hydroxysuccinimide-ester before anastomosis surgery in a murine model. RESULTS: Histological examination of both human and murine anastomoses revealed that closure of the serosal layer occurred first during the healing process. In vivo serosal ECM staining demonstrated that a significant portion of the newly formed ECM within the anastomosis was indeed deposited onto the serosal layer. Furthermore, mesenchymal cells within the anastomotic scar were positive for mesothelial cell markers, podoplanin and Wilms tumour protein. CONCLUSIONS: In this experimental study, the results suggest that serosal scar formation is an important mechanism for anastomotic integrity in intestinal anastomoses. Mesothelial cells may significantly contribute to scar formation during anastomotic healing through epithelial-to-mesenchymal transition, potentially suggesting a novel therapeutic target to prevent anastomotic leakage by enhancing physiological healing processes.

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

PURPOSE: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards. METHODS: We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs). RESULTS: The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics. CONCLUSION: This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

Ladies and Gentlemen! This is no humbug. Why Model-Guided Medicine will become a main pillar for the future healthcare system.

PURPOSE: Model-Guided Medicine (MGM) is a transformative approach to health care that offers a comprehensive and integrative perspective that goes far beyond our current concepts. In this editorial, we want to take a closer look at this innovative concept and how health care could benefit from its further development and application. METHODS: The information presented here is primarily the opinion of the authors and is based on their knowledge in the fields of information technology, computer science, and medicine. The contents are also the result of numerous discussions and scientific meetings within the CARS Society and the CARS Workshop on Model-Guided Medicine and are substantially stimulated by the available literature on the subject. RESULTS: The current healthcare landscape, with its reliance on isolated data points and broad population-based recommendations, often fails to integrate the dynamic and patient-specific factors necessary for truly personalised care. MGM addresses these limitations by integrating recent advancements in data processing, artificial intelligence, and human-computer interaction for the creation of individual models which integrate the available information and knowledge of patients, healthcare providers, devices, environment, etc. Based on a holistic concept, MGM will become effective tool for modern medicine, which shows a unique ability to assess and analyse interconnected relations and the combined impact of multiple factors on the individual. MGM emphasises transparency, reproducibility, and interpretability, ensuring that models are not black boxes but tools that healthcare professionals can fully understand, validate, and apply in clinical practice. CONCLUSION: The practical applications of MGM are vast, ranging from optimising individual treatment plans to enhancing the efficiency of entire healthcare systems. The research community is called upon to pioneer new projects that demonstrate MGM's potential, establishing it as a central pillar of future health care, where more personalised, predictive, and effective medical practices will hopefully become the standard.

Development of a human machine interface for robotically assisted surgery optimized for laparoscopic workflows.

INTRODUCTION: In robotic-assisted surgery (RAS), the input device is the primary site for the flow of information between the user and the robot. Most RAS systems remove the surgeon's console from the sterile surgical site. Beneficial for performing lengthy procedures with complex systems, this ultimately lacks the flexibility that comes with the surgeon being able to remain at the sterile site. METHODS: A prototype of an input device for RAS is constructed. The focus lies on intuitive control for surgeons and a seamless integration into the surgical workflow within the sterile environment. The kinematic design is translated from the kinematics of laparoscopic surgery. The input device uses three degrees of freedom from a flexible instrument as input. The prototype's performance is compared to that of a commercially available device in an evaluation. Metrics are used to evaluate the surgeons' performance with the respective input device in a virtual environment implemented for the evaluation. RESULTS: The evaluation of the two input devices shows statistically significant differences in the performance metrics. With the proposed prototype, the surgeons perform the tasks faster, more precisely, and with fewer errors. CONCLUSION: The prototype is an efficient and intuitive input device for surgeons with laparoscopic experience. The placement in the sterile working area allows for seamless integration into the surgical workflow and can potentially enable new robotic approaches.

Robotic scrub nurse to anticipate surgical instruments based on real-time laparoscopic video analysis.

BACKGROUND: Machine learning and robotics technologies are increasingly being used in the healthcare domain to improve the quality and efficiency of surgeries and to address challenges such as staff shortages. Robotic scrub nurses in particular offer great potential to address staff shortages by assuming nursing tasks such as the handover of surgical instruments. METHODS: We introduce a robotic scrub nurse system designed to enhance the quality of surgeries and efficiency of surgical workflows by predicting and delivering the required surgical instruments based on real-time laparoscopic video analysis. We propose a three-stage deep learning architecture consisting of a single frame-, temporal multi frame-, and informed model to anticipate surgical instruments. The anticipation model was trained on a total of 62 laparoscopic cholecystectomies. RESULTS: Here, we show that our prediction system can accurately anticipate 71.54% of the surgical instruments required during laparoscopic cholecystectomies in advance, facilitating a smoother surgical workflow and reducing the need for verbal communication. As the instruments in the left working trocar are changed less frequently and according to a standardized procedure, the prediction system works particularly well for this trocar. CONCLUSIONS: The robotic scrub nurse thus acts as a mind reader and helps to mitigate staff shortages by taking over a great share of the workload during surgeries while additionally enabling an enhanced process standardization.

Staff shortages in healthcare are an emerging problem leading to undersupply of medical experts such as scrub nurses in the operating room. The absence of these scrub nurses, who are responsible for providing surgical instruments, means that surgeries must be postponed or canceled. Robotic technologies and artificial intelligence offer great potential to address staff shortages in the operating room. We developed a robotic scrub nurse system that is able to take over the tasks of a human scrub nurse by delivering the required surgical tools. To maintain the pace of the surgery, our robotic scrub nurse system is also capable of predicting these required surgical tools in advance using artificial intelligence. The system is tested on laparoscopic cholecystectomies, a surgery, where the gallbladder is removed in a minimally invasive technique. We show that our prediction system can predict the majority of surgical instruments for this specific surgery facilitating a smoother surgical workflow and reducing the need for verbal communication. With further development, our system may help to cover the need for surgery while streamlining the surgical process through predictive support, potentially improving patient outcomes.

Towards multimodal graph neural networks for surgical instrument anticipation.

PURPOSE: Decision support systems and context-aware assistance in the operating room have emerged as the key clinical applications supporting surgeons in their daily work and are generally based on single modalities. The model- and knowledge-based integration of multimodal data as a basis for decision support systems that can dynamically adapt to the surgical workflow has not yet been established. Therefore, we propose a knowledge-enhanced method for fusing multimodal data for anticipation tasks. METHODS: We developed a holistic, multimodal graph-based approach combining imaging and non-imaging information in a knowledge graph representing the intraoperative scene of a surgery. Node and edge features of the knowledge graph are extracted from suitable data sources in the operating room using machine learning. A spatiotemporal graph neural network architecture subsequently allows for interpretation of relational and temporal patterns within the knowledge graph. We apply our approach to the downstream task of instrument anticipation while presenting a suitable modeling and evaluation strategy for this task. RESULTS: Our approach achieves an F1 score of 66.86% in terms of instrument anticipation, allowing for a seamless surgical workflow and adding a valuable impact for surgical decision support systems. A resting recall of 63.33% indicates the non-prematurity of the anticipations. CONCLUSION: This work shows how multimodal data can be combined with the topological properties of an operating room in a graph-based approach. Our multimodal graph architecture serves as a basis for context-sensitive decision support systems in laparoscopic surgery considering a comprehensive intraoperative operating scene.

Extra-abdominal trocar and instrument detection for enhanced surgical workflow understanding.

PURPOSE: Video-based intra-abdominal instrument tracking for laparoscopic surgeries is a common research area. However, the tracking can only be done with instruments that are actually visible in the laparoscopic image. By using extra-abdominal cameras to detect trocars and classify their occupancy state, additional information about the instrument location, whether an instrument is still in the abdomen or not, can be obtained. This can enhance laparoscopic workflow understanding and enrich already existing intra-abdominal solutions. METHODS: A data set of four laparoscopic surgeries recorded with two time-synchronized extra-abdominal 2D cameras was generated. The preprocessed and annotated data were used to train a deep learning-based network architecture consisting of a trocar detection, a centroid tracker and a temporal model to provide the occupancy state of all trocars during the surgery. RESULTS: The trocar detection model achieves an F1 score of 95.06 ± 0.88 % . The prediction of the occupancy state yields an F1 score of 89.29 ± 5.29 % , providing a first step towards enhanced surgical workflow understanding. CONCLUSION: The current method shows promising results for the extra-abdominal tracking of trocars and their occupancy state. Future advancements include the enlargement of the data set and incorporation of intra-abdominal imaging to facilitate accurate assignment of instruments to trocars.

Audio-based event detection in the operating room.

PURPOSE: Even though workflow analysis in the operating room has come a long way, current systems are still limited to research. In the quest for a robust, universal setup, hardly any attention has been given to the dimension of audio despite its numerous advantages, such as low costs, location, and sight independence, or little required processing power. METHODOLOGY: We present an approach for audio-based event detection that solely relies on two microphones capturing the sound in the operating room. Therefore, a new data set was created with over 63 h of audio recorded and annotated at the University Hospital rechts der Isar. Sound files were labeled, preprocessed, augmented, and subsequently converted to log-mel-spectrograms that served as a visual input for an event classification using pretrained convolutional neural networks. RESULTS: Comparing multiple architectures, we were able to show that even lightweight models, such as MobileNet, can already provide promising results. Data augmentation additionally improved the classification of 11 defined classes, including inter alia different types of coagulation, operating table movements as well as an idle class. With the newly created audio data set, an overall accuracy of 90%, a precision of 91% and a F1-score of 91% were achieved, demonstrating the feasibility of an audio-based event recognition in the operating room. CONCLUSION: With this first proof of concept, we demonstrated that audio events can serve as a meaningful source of information that goes beyond spoken language and can easily be integrated into future workflow recognition pipelines using computational inexpensive architectures.

The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis.

PURPOSE: Acoustic information can contain viable information in medicine and specifically in surgery. While laparoscopy depends mainly on visual information, our goal is to develop the means to capture and process acoustic information during laparoscopic surgery. METHODS: To achieve this, we iteratively developed three prototypes that will overcome the abdominal wall as a sound barrier and can be used with standard trocars. We evaluated them in terms of clinical applicability and sound transmission quality. Furthermore, the applicability of each prototype for sound classification based on machine learning was evaluated. RESULTS: Our developed prototypes for recording airborne sound from the intraperitoneal cavity represent a promising solution suitable for real-world clinical usage All three prototypes fulfill our set requirements in terms of clinical applicability (i.e., air-tightness, invasiveness, sterility) and show promising results regarding their acoustic characteristics and the associated results on ML-based sound classification. CONCLUSION: In summary, our prototypes for capturing acoustic information during laparoscopic surgeries integrate seamlessly with existing procedures and have the potential to augment the surgeon's perception. This advancement could change how surgeons interact with and understand the surgical field.

Model-based individual life-spanning documentation in visceral surgery: a proof of concept.

INTRODUCTION: Surgical documentation has many implications. However, its primary function is to transfer information about surgical procedures to other medical professionals. Thereby, written reports describing procedures in detail are the current standard, impeding comprehensive understanding of patient-individual life-spanning surgical course, especially if surgeries are performed at a timely distance and in diverse facilities. Therefore, we developed a novel model-based approach for documentation of visceral surgeries, denoted as 'Surgical Documentation Markup-Modeling' (SDM-M). MATERIAL AND METHODS: For scientific evaluation, we developed a web-based prototype software allowing for creating hierarchical anatomical models that can be modified by individual surgery-related markup information. Thus, a patient's cumulated 'surgical load' can be displayed on a timeline deploying interactive anatomical 3D models. To evaluate the possible impact on daily clinical routine, we performed an evaluation study with 24 surgeons and advanced medical students, elaborating on simulated complex surgical cases, once with classic written reports and once with our prototypical SDM-M software. RESULTS: Leveraging SDM-M in an experimental environment reduced the time needed for elaborating simulated complex surgical cases from 354 ± 85 s with the classic approach to 277 ± 128 s. (p = 0.00109) The perceived task load measured by the Raw NASA-TLX was reduced significantly (p = 0.00003) with decreased mental (p = 0.00004) and physical (p = 0.01403) demand. Also, time demand (p = 0.00041), performance (p = 0.00161), effort (p = 0.00024), and frustration (p = 0.00031) were improved significantly. DISCUSSION: Model-based approaches for life-spanning surgical documentation could improve the daily clinical elaboration and understanding of complex cases in visceral surgery. Besides reduced workload and time sparing, even a more structured assessment of individual surgical cases could foster improved planning of further surgeries, information transfer, and even scientific evaluation, considering the cumulative 'surgical load.' CONCLUSION: Life-spanning model-based documentation of visceral surgical cases could significantly improve surgery and workload.

Fractal analysis of extracellular matrix for observer-independent quantification of intestinal fibrosis in Crohn's disease.

Prevention of intestinal fibrosis remains an unresolved problem in the treatment of Crohn's disease (CD), as specific antifibrotic therapies are not yet available. Appropriate analysis of fibrosis severity is essential for assessing the therapeutic efficacy of potential antifibrotic drugs. The aim of this study was to develop an observer-independent method to quantify intestinal fibrosis in surgical specimens from patients with CD using structural analysis of the extracellular matrix (ECM). We performed fractal analysis in fibrotic and control histological sections of patients with surgery for CD (n = 28). To specifically assess the structure of the collagen matrix, polarized light microscopy was used. A score to quantify collagen fiber alignment and the color of the polarized light was established. Fractal dimension as a measure for the structural complexity correlated significantly with the histological fibrosis score whereas lacunarity as a measure for the compactness of the ECM showed a negative correlation. Polarized light microscopy to visualize the collagen network underlined the structural changes in the ECM network in advanced fibrosis. In conclusion, observer-independent quantification of the structural complexity of the ECM by fractal analysis is a suitable method to quantify the degree of intestinal fibrosis in histological samples from patients with CD.

Toward telemedical diagnostics-clinical evaluation of a robotic examination system for emergency patients.

Introduction: The SARS-CoV-2 pandemic has affected global public healthcare for several years. Numerous medical professionals have been infected since the outbreak in 2019, resulting in a shortage of healthcare providers. Since traditional personal protective wear was insufficient to eliminate the virus transmission reliably, new strategies to avoid cross-infection were imperative while enabling high-quality medical care. In the project ProteCT, we investigated the potential of robotic-assisted examination in providing medical examination via a telemedical approach. Material and Methods: We constructed a fully functional examination cabin equipped with cameras, microphones, screens and robotic arms to evaluate usability and perception. Therefore, we conducted a preliminary study with 10 healthy volunteers and 10 physicians to gain first insights and optimize the setup. In a second step, we performed telemedical examinations of actual patients from the local emergency department to compare the robotic approach with the classical method of measuring vital signs, auscultation, palpation and percussion. Results: The preliminary study identified basic requirements, such as the need for force-feedback and telemedical training for physicians. In the main study, acceptance was high and most patients indicated they would use a telemedical system again. Our setup enabled the physician to make the same diagnoses as by classic examination in the emergency department in most cases. Discussion: The potential acceptance of a telemedical system such as ProteCT is high. Robotic telemedical approaches could complement future healthcare beyond the Corona pandemic to reach rural areas or even war zones. Moreover, the daily clinical use of robotic telemedicine could improve patients' safety, the quality of perioperative management and the workflow in any medical facility. Conclusion: The development of telemedical and telerobotic systems is a multidisciplinary and complex challenge. However, acceptance of the proposed system was high among patients and physicians, indicating the potential use of similar systems for future healthcare.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.

PURPOSE: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. METHODS: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). RESULTS: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). CONCLUSION: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

Autonomous swab robot for naso- and oropharyngeal COVID-19 screening.

The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks and demands limited protective gear. In response to the COVID-19 pandemic, we developed and tested the first autonomous swab robot station for Naso- and Oropharyngeal Coronavirus Screening (SR-NOCS). A force-sensitive robot running under a Cartesian impedance controller is employed to drive the swab to the sampling area. This groundbreaking device underwent two clinical studies-one conducted during the initial pandemic lockdown in Europe (early 2021) and the other, more recently, in a public place after the pandemic had subsided earlier in the year 2023. In total, 52 patients suspected of COVID-19 infection were included in these clinical studies. The results revealed a complete positive correlation between autonomous and manual sampling. The test subjects exhibited a high acceptance rate, all expressing a willingness to undergo future tests with SR-NOCS. Based on our findings, such systems could enhance testing capabilities, potentially conducting up to 300 tests per robot per day with consistent precision. The tests can be carried out with minimal supervision, reducing infection risks and effectively safeguarding patients and healthcare workers.

[Mobile C-arm-Radiation exposure and workflow killer? : Potential of an innovative assistance system for intraoperative positioning]. / Mobiler C-Bogen ­ Strahlenbelastung und Workflow-Killer? : Potenzial eines innovativen Assistenzsystems für die intraoperative Positionierung.

Despite its versatile applicability the intraoperative use of a mobile C­arm is often problematic and potentially associated with increased radiation exposure for both the patient and the personnel. In particular, the correct positioning for adequate imaging can become a problem as the nonsterile circulating nurse has to coordinate the various maneuvers together with the surgeon without having a good view of the surgical field. The sluggishness of the equipment and the intraoperative setting (sterile borders, additional hardware, etc.) pose further challenges. A light detection and ranging (LIDAR)-based assistance system shows promise to provide accurate and intuitive repositioning support as part of an initial series of experimental trials. For this purpose, the sensors are attached to the C­arm base unit and enable navigation of the device in the operating room to a stored target position using a simultaneous localization and mapping (SLAM) algorithm. An improvement of the workflow as well as a reduction of radiation exposure represent the possible potential of this system. The advantages over other experimental approaches are the lack of external hardware and the ease of use without isolating the operator from the rest of the operating room environment; however, the suitability for daily use in the presence of additional interfering factors should be verified in further studies.

Versatile end effector for laparoscopic robotic scrub nurse.

PURPOSE: Integrating robotic scrub nurses in the operating room has the potential to help overcome staff shortages and limited use of available operating capacities in hospitals. Existing approaches of robotic scrub nurses are mainly focused on open surgical procedures, neglecting laparoscopic procedures. Laparoscopic interventions offer great potential for the context-sensitive integration of robotic systems due to possible standardization. However, the first step is to ensure the safe manipulation of laparoscopic instruments. METHODS: A robotic platform with a universal gripper system was designed to pick up and place laparoscopic as well as da Vinci[Formula: see text] instruments in an efficient workflow. The robustness of the gripper system was studied using a test protocol, which included a force absorption test to determine the operational safety limits of the design and a grip test to determine the system performance. RESULTS: The test protocol shows results regarding force and torque absorption capabilities of the end effector, which are essential when transferring an instrument to the surgeon to enable a robust handover. The grip tests show that the laparoscopic instruments can be safely picked up, manipulated and returned independent of unexpected positional deviations. The gripper system also enables the manipulation of da Vinci[Formula: see text] instruments, opening the door for robot-robot interaction. CONCLUSION: Our evaluation tests have shown that our robotic scrub nurse with the universal gripper system can safely and robustly manipulate laparoscopic and da Vinci[Formula: see text] instruments. The system design will continue with the integration of context-sensitive capabilities.

Demographic overview of pilonidal sinus carcinoma: updated insights into the incidence.

PURPOSE: There are only rough estimates of the worldwide incidence of pilonidal sinus carcinoma. The purpose of the study is to explore the demographic characteristics of this disease and to provide more precise information about its incidence. METHODS: The study included questioning the surgeons and pathologists in Germany in addition to a literature research. The literature investigation included all published articles about pilonidal carcinoma in all languages. The questionnaire included 1050 pathologists and all 834 hospitals with a surgical division in Germany. The outcome measures included the total number of cases, the language of publication, gender, age, country of origin, interval until the diagnosis of carcinoma, and reported incidence based on local studies. RESULTS: From 1900 to 2022, we found 140 cases of pilonidal sinus carcinoma in 103 articles. The investigation revealed two additional unpublished cases from Germany. The male-to-female ratio was 7.75:1. The countries with the most cases were the USA (35 cases, 25.0%), Spain (13 cases, 9.3%), and Turkey (11 cases, 7.6%). The average age was 54.0 ± 11.8 years and the interval between the diagnosis of the disease and the development of carcinoma was 20.1 ± 14.1 years. There was a parallel increase in reported cases of pilonidal sinus disease and pilonidal carcinoma over the last century. The reported incidence varied from 0.03% to 5.56%. The worldwide calculated incidence equaled 0.17%. CONCLUSION: Due to underreporting and other causes, the incidence of carcinoma emerging on the background of pilonidal sinus disease is higher than reported.