Background: Three-dimensional reconstructions of state-of-the-art high-resolution imaging are progressively being used more for preprocedural assessment in thoracic surgery. It is a promising tool that aims to improve patient-specific treatment planning, for example, for minimally invasive or robotic-assisted lung resections. Increasingly available mixed-reality hardware based on video pass-through technology enables the projection of image data as a hologram onto the patient. We describe the novel method of real-time 3D surgical planning in a mixed-reality setting by presenting three representative cases utilizing volume rendering. Materials: A mixed-reality system was set up using a high-performance workstation running a video pass-through-based head-mounted display. Image data from computer tomography were imported and volume-rendered in real-time to be customized through live editing. The image-based hologram was projected onto the patient, highlighting the regions of interest. Results: Three oncological cases were selected to explore the potentials of the mixed-reality system. Two of them presented large tumor masses in the thoracic cavity, while a third case presented an unclear lesion of the chest wall. We aligned real-time rendered 3D holographic image data onto the patient allowing us to investigate the relationship between anatomical structures and their respective body position. Conclusions: The exploration of holographic overlay has proven to be promising in improving preprocedural surgical planning, particularly for complex oncological tasks in the thoracic surgical field. Further studies on outcome-related surgical planning and navigation should therefore be conducted. Ongoing technological progress of extended reality hardware and intelligent software features will most likely enhance applicability and the range of use in surgical fields within the near future.

Zusammenfassung: Erweiterte Realitäten (XR) mit den Unterbegriffen Virtual Reality (VR), Augmented Reality (AR) und Mixed Reality (MR) beschreiben interaktive und immersive Technologien, welche die reelle Welt durch digitale Elemente ersetzen oder diese um solche nahtlos erweitern. Die XR bieten somit ein sehr breites mögliches Anwendungsspektrum in der Medizin. In der Chirurgie und insbesondere in der Thoraxchirurgie lassen sich XR-Technologien für Anwendungen der Behandlungsplanung, Navigation, Ausbildung und Patienteninformationen nutzbar machen. Solche Anwendungsfälle befinden sich zunehmend in der Erprobung und müssen hinsichtlich ihrer Wertigkeit evaluiert werden. Wir geben einen Überblick über den Status quo der technischen Entwicklung, aktuelle chirurgische Einsatzgebiete der XR und blicken in die Zukunft der medizinischen XR-Landschaft mit Integration von künstlicher Intelligenz (KI).

The integration of extended reality (XR) technologies into health care procedures presents transformative opportunities, particularly in surgical processes. This study delves into the utilization of virtual reality (VR) for preoperative planning related to chest wall resections in thoracic surgery. Leveraging the capabilities of 3-dimensional (3D) imaging, real-time visualization, and collaborative VR environments, surgeons gain enhanced anatomical insights and can develop predictive surgical strategies. Two clinical cases highlighted the effectiveness of this approach, showcasing the potential for personalized and intricate surgical planning. The setup provides an immersive, dynamic representation of real patient data, enabling collaboration among teams from separate locations. While VR offers enhanced interactive and visualization capabilities, preliminary evidence suggests it may support more refined preoperative strategies, potentially influence postoperative outcomes, and optimize resource management. However, its comparative advantage over traditional methods needs further empirical validation. Emphasizing the potential of XR, this exploration suggests its broad implications in thoracic surgery, especially when dealing with complex cases requiring multidisciplinary collaboration in the immersive virtual space, often referred to as the metaverse. This innovative approach necessitates further examination, marking a shift toward future surgical preparations. In this article, we sought to demonstrate the technique of an immersive real-time volume-rendered collaborative VR-planning tool using exemplary case studies in chest wall surgery.

Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.

Background: Approximately 5 billion people worldwide lack adequate access to surgical care, primarily in the Global South. Especially in crisis regions and war zones, telemedical applications may enhance health services. This study explores the feasibility of using artificial intelligence (AI)-enhanced 3D imaging and extended reality (XR) technologies for intercontinental surgical case conferences in a low-resource scenario in Uganda. Our pilot study aims to assess the value of these technologies to address the lack of surgical resources and multilateral knowledge exchange. Objective: This study intends to determine the feasibility of using new AI-enhanced image modeling technology within an immersive spatial XR scenario to collaboratively and remotely assess reconstructive patient cases in the resource-limited country of Uganda. Methods: Within a surgical camp at Lamu Medical Centre, Uganda, 3D models of patients’ conditions were created using a smartphone app. Digital models were generated from photographs taken on-site and processed into 3D formats to be visualized in virtual case conferences. Here, surgeons from Uganda and Germany used virtual reality (VR) headsets to collaboratively discuss case strategies while marking surgical approaches on each digital patient model. Results: The study included 15 patients requiring reconstructive surgery, with a diverse range of conditions. The use of XR technology facilitated detailed visualization and discussion of surgical strategies. The process was time-efficient, with a total of under 8 minutes per case for data acquisition and model creation, and resource-efficient with surgeons reporting sufficient quality of smartphone-derived models. Valuable user experience and precise interaction during the VR case processing were found, underlining its potential to improve surgical planning and patient care in resource-limited settings. Conclusions: The findings indicate that AI-enhanced 3D imaging and immersive virtual communication platforms are valuable tools for integrative surgical case assessments. The cost-effectiveness of the used consumer solutions should be especially beneficial for low-resource environments. While the study demonstrates the feasibility of this approach, further research is needed to explore a broader application and impact of these technologies in global health. The study highlights the potential of XR to enhance training and surgical precision, contributing to better health care outcomes in underserved regions.

Objectives: The integration of advanced technologies is transforming surgical practice, particularly through robotic systems. This study presents the early clinical implementation of the Dexter ^® surgical robot for cholecystectomy and evaluates clinical outcomes using the IDEAL framework. Methods: Twenty patients underwent elective robotic-assisted cholecystectomy using the Dexter ^® robot. A thorough implementation process, including rigorous surgeon and nurse training and standardized care protocols, was established. Data on operative metrics, complications, and patient outcomes were analyzed, and patient well-being was assessed via a postoperative phone survey. Results: Six surgeons and thirty nurses were trained, with surgeons completing a minimum of 20 h of simulation. Preoperative and operative times were significantly reduced through this process. Comparing the first 10 operations to the second, docking time decreased from 11.4 ± 4.1 min to 7.1 ± 2.1 min (p=0.0144) and operative time improved from 130.5 ± 25.7 min to 99.7 ± 21.8 min (p=0.0134). Mean intraoperative blood loss was minimal, averaging 19.5 ± 31.4 mL, and the average length of hospital stay was 3.1 ± 1.4 days. Postoperative pain levels were low, and patient satisfaction was high, as assessed by telephone survey. Conclusions: Our findings highlight the value of the IDEAL framework in guiding the systematic evaluation and implementation of new surgical technologies such as the Dexter ^® robot. A structured approach is essential to improve patient outcomes and safety in the coming digital transformation of surgery.