Computer-assisted abdominal surgery: new technologies

Comparing a virtual reality head-mounted display to on-screen three-dimensional visualization and two-dimensional computed tomography data for training in decision making in hepatic surgery: a randomized controlled study

OBJECTIVE

Evaluation of the benefits of a virtual reality (VR) environment with a head-mounted display (HMD) for decision-making in liver surgery.

BACKGROUND

Training in liver surgery involves appraising radiologic images and considering the patient's clinical information. Accurate assessment of 2D-tomography images is complex and requires considerable experience, and often the images are divorced from the clinical information. We present a comprehensive and interactive tool for visualizing operation planning data in a VR environment using a head-mounted-display and compare it to 3D visualization and 2D-tomography.

METHODS

Ninety medical students were randomized into three groups (1:1:1 ratio). All participants analyzed three liver surgery patient cases with increasing difficulty. The cases were analyzed using 2D-tomography data (group "2D"), a 3D visualization on a 2D display (group "3D") or within a VR environment (group "VR"). The VR environment was displayed using the "Oculus Rift ™" HMD technology. Participants answered 11 questions on anatomy, tumor involvement and surgical decision-making and 18 evaluative questions (Likert scale).

RESULTS

Sum of correct answers were significantly higher in the 3D (7.1 ± 1.4, p < 0.001) and VR (7.1 ± 1.4, p < 0.001) groups than the 2D group (5.4 ± 1.4) while there was no difference between 3D and VR (p = 0.987). Times to answer in the 3D (6:44 ± 02:22 min, p < 0.001) and VR (6:24 ± 02:43 min, p < 0.001) groups were significantly faster than the 2D group (09:13 ± 03:10 min) while there was no difference between 3D and VR (p = 0.419). The VR environment was evaluated as most useful for identification of anatomic anomalies, risk and target structures and for the transfer of anatomical and pathological information to the intraoperative situation in the questionnaire.

CONCLUSIONS

A VR environment with 3D visualization using a HMD is useful as a surgical training tool to accurately and quickly determine liver anatomy and tumor involvement in surgery.

Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery

BACKGROUND

Image-guidance promises to make complex situations in liver interventions safer. Clinical success is limited by intraoperative organ motion due to ventilation and surgical manipulation. The aim was to assess influence of different ventilatory and operative states on liver motion in an experimental model.

METHODS

Liver motion due to ventilation (expiration, middle, and full inspiration) and operative state (native, laparotomy, and pneumoperitoneum) was assessed in a live porcine model (n = 10). Computed tomography (CT)-scans were taken for each pig for each possible combination of factors. Liver motion was measured by the vectors between predefined landmarks along the hepatic vein tree between CT scans after image segmentation.

RESULTS

Liver position changed significantly with ventilation. Peripheral regions of the liver showed significantly higher motion (maximal Euclidean motion 17.9 ± 2.7 mm) than central regions (maximal Euclidean motion 12.6 ± 2.1 mm, p < 0.001) across all operative states. The total average motion measured 11.6 ± 0.7 mm (p < 0.001). Between the operative states, the position of the liver changed the most from native state to pneumoperitoneum (14.6 ± 0.9 mm, p < 0.001). From native state to laparotomy comparatively, the displacement averaged 9.8 ± 1.2 mm (p < 0.001). With pneumoperitoneum, the breath-dependent liver motion was significantly reduced when compared to other modalities. Liver motion due to ventilation was 7.7 ± 0.6 mm during pneumoperitoneum, 13.9 ± 1.1 mm with laparotomy, and 13.5 ± 1.4 mm in the native state (p < 0.001 in all cases).

CONCLUSIONS

Ventilation and application of pneumoperitoneum caused significant changes in liver position. Liver motion was reduced but clearly measurable during pneumoperitoneum. Intraoperative guidance/navigation systems should therefore account for ventilation and intraoperative changes of liver position and peripheral deformation.

Current advancements in therapeutic approaches in orthopedic surgery: a review of recent trends

Recent advancements in orthopedic surgery have greatly improved the management of musculoskeletal disorders and injuries. This review discusses the latest therapeutic approaches that have emerged in orthopedics. We examine the use of regenerative medicine, including stem cell therapy and platelet-rich plasma (PRP) injections, to accelerate healing and promote tissue regeneration. Additionally, we explore the application of robotic-assisted surgery, which provides greater precision and accuracy during surgical procedures. We also delve into the emergence of personalized medicine, which tailors treatments to individual patients based on their unique genetic and environmental factors. Furthermore, we discuss telemedicine and remote patient monitoring as methods for improving patient outcomes and reducing healthcare costs. Finally, we examine the growing interest in using artificial intelligence and machine learning in orthopedics, particularly in diagnosis and treatment planning. Overall, these advancements in therapeutic approaches have significantly improved patient outcomes, reduced recovery times, and enhanced the overall quality of care in orthopedic surgery.

Enhancing visualization and guidance in general surgery: a comprehensive and narrative review of the current cutting-edge technologies and future perspectives

BACKGROUND

In the last decade, there has been a great effort in developing new technologies to enhance surgical visualization and guidance. This comprehensive and narrative review aimed to provide a wide and extensive overview of the current state of the art on this topic and their near-future perspectives linked to the development of artificial intelligence (AI), by focusing on the most recent and relevant literature.

METHODS

A comprehensive and narrative review of the literature was performed by searching specific terms on PubMed/MEDLINE, Scopus, and Embase databases, assessing the current state of the art on this topic.

RESULTS

Fluorescence-guided surgery, contrast-enhanced ultrasound (CEUS), ultra-high frequency ultrasound (UHFUS), photoacoustic imaging (PAI), and augmented reality (AR) are boosting the field of image-guided techniques as the rapid development of AI in surgery is promising a more automated decision-making and surgical movements in the operating room.

CONCLUSION

Fluorescence-guided surgery, CEUS, UHFUS, PAI, and AR are becoming crucial to give surgeons a new level of information during the intervention, with the right timing and sequence, and represent the future of surgery. As many more controlled studies are needed to validate the employment of these technologies, the next generation of surgeons must become more familiar with the basics of AI to better incorporate new tools into the daily surgical practice of the future.

Clean visual field reconstruction in robot-assisted laparoscopic surgery based on dynamic prediction

Robot-assisted minimally invasive surgery has been broadly employed in complicated operations. However, the multiple surgical instruments may occupy a large amount of visual space in complex operations performed in narrow spaces, which affects the surgeon's judgment on the shape and position of the lesion as well as the course of its adjacent vessels/lacunae. In this paper, a surgical scene reconstruction method is proposed, which involves the tracking and removal of surgical instruments and the dynamic prediction of the obscured region. For tracking and segmentation of instruments, the image sequences are preprocessed by a modified U-Net architecture composed of a pre-trained ResNet101 encoder and a redesigned decoder. Also, the segmentation boundaries of the instrument shafts are extended using image filtering and a real-time index mask algorithm to achieve precise localization of the obscured elements. For predicting the deformation of soft tissues, a soft tissue deformation prediction algorithm is proposed based on dense optical flow gravitational field and entropy increase, which can achieve local dynamic visualization of the surgical scene by integrating image morphological operations. Finally, the preliminary experiments and the pre-clinical evaluation were presented to demonstrate the performance of the proposed method. The results show that the proposed method can provide the surgeon with a clean and comprehensive surgical scene, reconstruct the course of important vessels/lacunae, and avoid inadvertent injuries.

The role of artificial intelligence in the future of urogynecology

Artificial intelligence (AI) in medicine is a rapidly growing field aimed at using machine learning models to improve health outcomes and patient experiences. Many new platforms have become accessible and therefore it seems inevitable that we consider how to implement them in our day-to-day practice. Currently, the specialty of urogynecology faces new challenges as the population grows, life expectancy increases, and quality of life expectation is much improved. As AI has a lot of potential to promote the discipline of urogynecology, we aim to explore its abilities and possible use in the future. Challenges and risks are associated with using AI, and a responsible use of such resources is required.

The Economic Impact of Standardization and Digitalization in the Operating Room: A Systematic Literature Review

Hospital face increased resource constraints and competition. This escalates the need for efficiency optimization especially in resource-intense areas, such as the Operating Room (OR). Efficiency cannot happen at expenses of patient outcomes. Innovative digital support systems (DSS) have been introduced into the market to support established standardization methods of intraoperative workflows further. This review aimed to analyze whether applied standardization methods and implemented DSS of intraoperative surgical workflows lead to increasing efficiency and demonstrate economic improvements. A systematic review of intraoperative surgical workflows standardization and digitalization was performed. Journal articles and reviews from 2000 to 2023 were retrieved from EBSCO, PubMed, and Scopus databases, as well as the internal database of Johnson & Johnson. 17 articles showed a significant increase in efficiency through standardization, which led to cost reductions between $70.20 to $3,516 per case without negatively impacting quality. Five additional articles on DSS demonstrated a significant positive impact on efficiency and quality. Reduction in OR-time between 6 to 22% per case was one main contributor. No literature on DSS revealed any correlated economic impact. Selected standardization methods and introduced DSS for intraoperative surgical workflows effectively increase efficiency while maintaining or even improving quality. Demonstrated cost-effectiveness of non-digital standardization methods across surgical areas requires more research on complex and resource-intensive procedures and the economic value of DSS to support hospital management's strategic decisions to overcome the increasing economic burden.

Evaluation of AR visualization approaches for catheter insertion into the ventricle cavity

Augmented reality (AR) has shown potential in computer-aided surgery. It allows for the visualization of hidden anatomical structures as well as assists in navigating and locating surgical instruments at the surgical site. Various modalities (devices and/or visualizations) have been used in the literature, but few studies investigated the adequacy/superiority of one modality over the other. For instance, the use of optical see-through (OST) HMDs has not always been scientifically justified. Our goal is to compare various visualization modalities for catheter insertion in external ventricular drain and ventricular shunt procedures. We investigate two AR approaches: (1) 2D approaches consisting of a smartphone and a 2D window visualized through an OST (Microsoft HoloLens 2), and (2) 3D approaches consisting of a fully aligned patient model and a model that is adjacent to the patient and is rotationally aligned using an OST. 32 participants joined this study. For each visualization approach, participants were asked to perform five insertions after which they filled NASA-TLX and SUS forms. Moreover, the position and orientation of the needle with respect to the planning during the insertion task were collected. The results show that participants achieved a better insertion performance significantly under 3D visualizations, and the NASA-TLX and SUS forms reflected the preference of participants for these approaches compared to 2D approaches.

Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study

BACKGROUND

Recognition of the inferior mesenteric artery (IMA) during colorectal cancer surgery is crucial to avoid intraoperative hemorrhage and define the appropriate lymph node dissection line. This retrospective feasibility study aimed to develop an IMA anatomical recognition model for laparoscopic colorectal resection using deep learning, and to evaluate its recognition accuracy and real-time performance.

METHODS

A complete multi-institutional surgical video database, LapSig300 was used for this study. Intraoperative videos of 60 patients who underwent laparoscopic sigmoid colon resection or high anterior resection were randomly extracted from the database and included. Deep learning-based semantic segmentation accuracy and real-time performance of the developed IMA recognition model were evaluated using Dice similarity coefficient (DSC) and frames per second (FPS), respectively.

RESULTS

In a fivefold cross-validation conducted using 1200 annotated images for the IMA semantic segmentation task, the mean DSC value was 0.798 (± 0.0161 SD) and the maximum DSC was 0.816. The proposed deep learning model operated at a speed of over 12 FPS.

CONCLUSION

To the best of our knowledge, this is the first study to evaluate the feasibility of real-time vascular anatomical navigation during laparoscopic colorectal surgery using a deep learning-based semantic segmentation approach. This experimental study was conducted to confirm the feasibility of our model; therefore, its safety and usefulness were not verified in clinical practice. However, the proposed deep learning model demonstrated a relatively high accuracy in recognizing IMA in intraoperative images. The proposed approach has potential application in image navigation systems for unfixed soft tissues and organs during various laparoscopic surgeries.

Artificial intelligence as a future in cancer surgery

Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision-making. Machine learning and deep learning (DL) are subfields of AI that are able to learn from experience in order to complete tasks. AI and its subfields, in particular DL, have been applied in numerous fields of medicine, especially in the cure of cancer. Computer vision (CV) system has improved diagnostic accuracy both in histopathology analyses and radiology. In surgery, CV has been used to design navigation system and robotic-assisted surgical tools that increased the safety and efficiency of oncological surgery by minimizing human error. By learning the basis of AI, surgeons can take part in this revolution to optimize surgical care of oncologic disease.

Current applications of artificial intelligence in vascular surgery

Basic foundations of artificial intelligence (AI) include analyzing large amounts of data, recognizing patterns, and predicting outcomes. At the core of AI are well-defined areas, such as machine learning, natural language processing, artificial neural networks, and computer vision. Although research and development of AI in health care is being conducted in many medical subspecialties, only a few applications have been implemented in clinical practice. This is true in vascular surgery, where applications are mostly in the translational research stage. These AI applications are being evaluated in the realms of vascular diagnostics, perioperative medicine, risk stratification, and outcome prediction, among others. Apart from the technical challenges of AI and research outcomes on safe and beneficial use in patient care, ethical issues and policy surrounding AI will present future challenges for its successful implementation. This review will give a brief overview and a basic understanding of AI and summarize the currently available and used clinical AI applications in vascular surgery.

AI in healthcare: A narrative review

In this paper, we focus on providing a narrative review of healthcare services in which artificial intelligence (AI) based services are used as part of the operations and analyze key elements to create successful AI-based services for healthcare. The benefits of AI in healthcare are measured by how AI is improving the healthcare outcomes, help caregivers in work, and reducing healthcare costs. AI market in healthcare sector have also a high market potential with 28% global compound annual growth rate. This paper will collect outcomes from multiple perspectives of healthcare sector including financial, health improvement, and care outcome as well as provide proposals and key factors for successful implementation of AI methods in healthcare. It is shown in this paper that AI implementation in healthcare can provide cost reduction and same time provide better health outcome for all.

Right-sided Bochdalek hernia in an adult with hepatic malformation and intestinal malrotation

Background

Bochdalek hernia is a common congenital diaphragmatic defect that usually manifests with cardiopulmonary insufficiency in neonates. It is very rare in adults, and symptomatic cases are mostly left-sided. Diaphragmatic defects generally warrant immediate surgical intervention to reduce the risk of incarceration or strangulation of the displaced viscera.

Case presentation

A 47-year-old woman presented with dyspnea on exertion. Computed tomography revealed that a large part of the intestinal loop with superior mesenteric vessels and the right kidney were displaced into the right thoracic cavity. Preoperative three-dimensional (3D) simulation software visualized detailed anatomy of displaced viscera and the precise location and size of the diaphragmatic defect. She underwent elective surgery after concomitant pulmonary hypertension was stabilized preoperatively. The laparotomic approach was adopted. Malformation of the liver and the presence of intestinal malrotation were confirmed during the operation. The distal part of the duodenum, jejunum, ileum, colon, and right kidney were reduced into the abdominal cavity consecutively. A large-sized oval defect was closed with monofilament polypropylene mesh. No complications occurred postoperatively.

Conclusion

Symptomatic right-sided Bochdalek hernia in adults is exceedingly rare and is frequently accompanied by various visceral anomalies. Accurate diagnosis and appropriate surgical repair are crucial to prevent possible incarceration or strangulation. The preoperative 3D simulation provided comprehensive information on anatomy and concomitant anomalies and helped surgeons plan the operation meticulously and perform procedures safely.

Computer-assisted technology for enhanced abdominal surgery

The application of computer-based technology to surgery has the potential to enhance the accuracy and outcomes of surgical procedures and perioperative care. Such innovative technologies include the integration of artificial intelligence into surgical decision-making, and the use of three-dimensional (3D) visual imaging, other real-time imaging techniques, and 3D printing technology.

IMHOTEP: cross-professional evaluation of a three-dimensional virtual reality system for interactive surgical operation planning, tumor board discussion and immersive training for complex liver surgery in a head-mounted display

BACKGROUND

Virtual reality (VR) with head-mounted displays (HMD) may improve medical training and patient care by improving display and integration of different types of information. The aim of this study was to evaluate among different healthcare professions the potential of an interactive and immersive VR environment for liver surgery that integrates all relevant patient data from different sources needed for planning and training of procedures.

METHODS

3D-models of the liver, other abdominal organs, vessels, and tumors of a sample patient with multiple hepatic masses were created. 3D-models, clinical patient data, and other imaging data were visualized in a dedicated VR environment with an HMD (IMHOTEP). Users could interact with the data using head movements and a computer mouse. Structures of interest could be selected and viewed individually or grouped. IMHOTEP was evaluated in the context of preoperative planning and training of liver surgery and for the potential of broader surgical application. A standardized questionnaire was voluntarily answered by four groups (students, nurses, resident and attending surgeons).

RESULTS

In the evaluation by 158 participants (57 medical students, 35 resident surgeons, 13 attending surgeons and 53 nurses), 89.9% found the VR system agreeable to work with. Participants generally agreed that complex cases in particular could be assessed better (94.3%) and faster (84.8%) with VR than with traditional 2D display methods. The highest potential was seen in student training (87.3%), resident training (84.6%), and clinical routine use (80.3%). Least potential was seen in nursing training (54.8%).

CONCLUSIONS

The present study demonstrates that using VR with HMD to integrate all available patient data for the preoperative planning of hepatic resections is a viable concept. VR with HMD promises great potential to improve medical training and operation planning and thereby to achieve improvement in patient care.

Effects of laparoscopy, laparotomy, and respiratory phase on liver volume in a live porcine model for liver resection

Background

Hepatectomy, living donor liver transplantations and other major hepatic interventions rely on precise calculation of the total, remnant and graft liver volume. However, liver volume might differ between the pre- and intraoperative situation. To model liver volume changes and develop and validate such pre- and intraoperative assistance systems, exact information about the influence of lung ventilation and intraoperative surgical state on liver volume is essential.

Methods

This study assessed the effects of respiratory phase, pneumoperitoneum for laparoscopy, and laparotomy on liver volume in a live porcine model. Nine CT scans were conducted per pig (N = 10), each for all possible combinations of the three operative (native, pneumoperitoneum and laparotomy) and respiratory states (expiration, middle inspiration and deep inspiration). Manual segmentations of the liver were generated and converted to a mesh model, and the corresponding liver volumes were calculated.

Results

With pneumoperitoneum the liver volume decreased on average by 13.2% (112.7 ml ± 63.8 ml, p < 0.0001) and after laparotomy by 7.3% (62.0 ml ± 65.7 ml, p = 0.0001) compared to native state. From expiration to middle inspiration the liver volume increased on average by 4.1% (31.1 ml ± 55.8 ml, p = 0.166) and from expiration to deep inspiration by 7.2% (54.7 ml ± 51.8 ml, p = 0.007).

Conclusions

Considerable changes in liver volume change were caused by pneumoperitoneum, laparotomy and respiration. These findings provide knowledge for the refinement of available preoperative simulation and operation planning and help to adjust preoperative imaging parameters to best suit the intraoperative situation.

Role of Artificial Intelligence (AI) in Surgery: Introduction, General Principles, and Potential Applications

AI (Artificial intelligence) is an interdisciplinary field aimed at the development of algorithms to endow machines with the capability of executing cognitive tasks. The number of publications regarding AI and surgery has increased dramatically over the last two decades. This phenomenon can partly be explained by the exponential growth in computing power available to the largest AI training runs. AI can be classified into different sub-domains with extensive potential clinical applications in the surgical setting. AI will increasingly become a major component of clinical practice in surgery. The aim of the present Narrative Review is to give a general introduction and summarized overview of AI, as well as to present additional remarks on potential surgical applications and future perspectives in surgery.

Assessment and application of the coherent point drift algorithm to augmented reality surgical navigation for laparoscopic partial nephrectomy

PURPOSE

The surface-based registration approach to laparoscopic augmented reality (AR) has clear advantages. Nonrigid point-set registration paves the way for surface-based registration. Among current non-rigid point set registration methods, the coherent point drift (CPD) algorithm is rarely used because of two challenges: (1) volumetric deformation is difficult to predict, and (2) registration from intraoperative visible tissue surface to whole anatomical preoperative model is a "part-to-whole" registration that CPD cannot be applied directly to. We preliminarily applied CPD on surgical navigation for laparoscopic partial nephrectomy (LPN). However, it introduces normalization errors and lacks navigation robustness. This paper presents important advances for more effectively applying CPD to LPN surgical navigation while attempting to quantitatively evaluate the accuracy of CPD-based surgical navigation.

METHODS

First, an optimized volumetric deformation (Op-VD) algorithm is proposed to achieve accurate prediction of volume deformation. Then, a projection-based partial selection method is presented to conveniently and robustly apply the CPD to LPN surgical navigation. Finally, kidneys with different deformations in vitro, phantom and in vivo experiments are performed to evaluate the accuracy and effectiveness of our approach.

RESULTS

The average root-mean-square error of volume deformation was refined to 0.84 mm. The mean target registration error (TRE) of the surface and inside markers in the in vitro experiments decreased to 1.51 mm and 1.29 mm, respectively. The robustness and precision of CPD-based navigation were validated in phantom and in vivo experiments, and the mean navigation TRE of the phantom experiments was found to be [Formula: see text] mm.

CONCLUSION

Accurate volumetric deformation and robust navigation results can be achieved in AR navigation of LPN by using surface-based registration with CPD. Evaluation results demonstrate the effectiveness of our proposed methods while showing the clinical application potential of CPD. This work has important guiding significance for the application of the CPD in laparoscopic AR.

A Multimodal Pancreas Phantom for Computer-Assisted Surgery Training

Training of surgical residents and the establishment of innovative surgical techniques require training phantoms that realistically mimic human anatomy. Because animal models have their limitations due to ethical aspects, costs, and the required efforts to set up such training, artificial phantoms are a promising alternative. In the field of image-guided surgery, the challenge lies in developing phantoms that are accurate both anatomically and in terms of imaging properties, while taking the cost factor into account. With respect to the pancreas, animal models are less suitable because their anatomy differs significantly from human anatomy and tissue properties rapidly degrade in the case of ex vivo models. Nevertheless, progress with artificial phantoms has been sparse, although the need for innovative, minimally invasive therapies that require adequate training is steadily increasing. Methods: In the course of this project, an artificial pancreas phantom that is compatible with basic electrosurgical techniques was developed with realistic anatomic and haptic properties, computed tomography, and ultrasound imaging capabilities. This article contains step-by-step instructions for the fabrication of a low-cost pancreatic phantom. The molds are also available for download in a 3D file format. Results: The phantom was successfully validated with regard to its computed tomography and ultrasound properties. As a result, the phantom could be used in combination with a state-of-the-art computer-assisted navigation system. The resection capabilities were positively evaluated in a preclinical study evaluating endoscopic resections using the navigation system. Finally, the durability of the phantom material was tested in a study with multiple needle insertions. Conclusion: The developed phantom represents an open-access and low-cost durable alternative to conventional animal models in the continuous process of surgical training and development of new techniques.

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.

[Artificial intelligence in pediatric surgery : Present and future]

BACKGROUND

Artificial intelligence, automatization and digital transformation increasingly dominate the business models of almost all enterprises. Even in medicine and medical technology, companies also no longer close their minds to this development as the advantages provided by the new ideas and processes in medicine and particularly in compact disciplines, such as pediatric surgery have occasionally been recognized.

OBJECTIVE

This article gives a status quo analysis of artificial intelligence in international pediatric surgery with a discussion of future perspectives and suggestions from the authors' perspective.

MATERIAL AND METHODS

Appraisal and discussion of international publications, external expert opinions and personal experiences of the authors.

RESULTS

A wide spectrum of applications using artificial intelligence in surgery is internationally available. Many of these developments can also be further adapted for use in pediatric surgery. The experience using artificial intelligence for special pediatric surgical indications is currently limited to isolated cases.

CONCLUSION

Disciplines such as pediatric surgery cannot disregard the trend towards the application of artificial intelligence in daily practice. In addition to the establishment of current developments, the requirements of pediatric surgery should also be taken into account. These were some of the impulses that led to the founding of the working group on digitalization of the German Association for Pediatric Surgery in September 2019.

Evaluating the impact of image guidance in the surgical setting: a systematic review

Background

Image guidance has been clinically available for over a period of 20 years. Although research increasingly has a translational emphasis, overall the clinical uptake of image guidance systems in surgery remains low. The objective of this review was to establish the metrics used to report on the impact of surgical image guidance systems used in a clinical setting.

Methods

A systematic review of the literature was carried out on all relevant publications between January 2000 and April 2016. Ovid MEDLINE and Embase databases were searched using a title strategy. Reported outcome metrics were grouped into clinically relevant domains and subsequent sub-categories for analysis.

Results

In total, 232 publications were eligible for inclusion. Analysis showed that clinical outcomes and system interaction were consistently reported. However, metrics focusing on surgeon, patient and economic impact were reported less often. No increase in the quality of reporting was observed during the study time period, associated with study design, or when the clinical setting involved a surgical specialty that had been using image guidance for longer.

Conclusions

Publications reporting on the clinical use of image guidance systems are evaluating traditional surgical outcomes and neglecting important human and economic factors, which are pertinent to the uptake, diffusion and sustainability of image-guided surgery. A framework is proposed to assist researchers in providing comprehensive evaluation metrics, which should also be considered in the design phase. Use of these would help demonstrate the impact in the clinical setting leading to increased clinical integration of image guidance systems.

Electronic supplementary material

The online version of this article (10.1007/s00464-019-06876-x) contains supplementary material, which is available to authorized users.

Artificial Intelligence in Surgery: Promises and Perils

OBJECTIVE

The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments.

SUMMARY BACKGROUND DATA

AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers.

METHODS

A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed.

RESULTS

Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed.

CONCLUSIONS

Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.

Enhanced Visualization: From Intraoperative Tissue Differentiation to Augmented Reality

Background

Optimal visualization of the operative field and methods that additionally provide supportive optical information form the basis for target-directed and successful surgery. This article strives to give an overview of current enhanced visualization techniques in visceral surgery and to highlight future developments.

Methods

The article was written as a comprehensive review on this topic and is based on a MEDLINE search and ongoing research from our own group and from other working groups.

Results

Various techniques for enhanced visualization are described comprising augmented reality, unspecific and targeted staining methods, and optical modalities such as narrow-band imaging. All facilitate our surgical performance; however, due to missing randomized controlled studies for most of the innovations reported on, the available evidence is low.

Conclusion

Many new visualization technologies are emerging with the aim to improve our perception of the surgical field leading to less invasive, target-oriented, and elegant treatment forms that are of significant benefit to our patients.

[Comprehensive system integration and networking in operating rooms]

BACKGROUND

A comprehensive surveillance and control system integrating all devices and functions is a precondition for realization of the operating room of the future.

STATE OF THE ART

Multiple proprietary integrated operation room systems are currently available with a central user interface; however, they only cover a relatively small part of all functionalities.

INNOVATIVE APPROACHES

Internationally, there are at least three different initiatives to promote a comprehensive systems integration and networking in the operating room: the Japanese smart cyber operating theater (SCOT), the American medical device plug-and-play interoperability program (MDPnP) and the German secure and dynamic networking in operating room and hospital (OR.NET) project supported by the Federal Ministry of Education and Research.

PRELIMINARY RESULTS

Within the framework of the internationally advanced OR.NET project, prototype solution approaches were realized, which make short-term and mid-term comprehensive data retrieval systems probable. An active and even autonomous control of the medical devices by the surveillance and control system (closed loop) is expected only in the long run due to strict regulatory barriers.

[Intelligent operating room suite : From passive medical devices to the self-thinking cognitive surgical assistant]

Modern operating room (OR) suites are mostly digitally connected but until now the primary focus was on the presentation, transfer and distribution of images. Device information and processes within the operating theaters are barely considered. Cognitive assistance systems have triggered a fundamental rethinking in the automotive industry as well as in logistics. In principle, tasks in the OR, some of which are highly repetitive, also have great potential to be supported by automated cognitive assistance via a self-thinking system. This includes the coordination of the entire workflow in the perioperative process in both the operating theater and the whole hospital. With corresponding data from hospital information systems, medical devices and appropriate models of the surgical process, intelligent systems could optimize the workflow in the operating theater in the near future and support the surgeon. Preliminary results on the use of device information and automatically controlled OR suites are already available. Such systems include, for example the guidance of laparoscopic camera systems. Nevertheless, cognitive assistance systems that make use of knowledge about patients, processes and other pieces of information to improve surgical treatment are not yet available in the clinical routine but are urgently needed in order to automatically assist the surgeon in situation-related activities and thus substantially improve patient care.

An introduction and overview of machine learning in neurosurgical care

BACKGROUND

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care.

METHOD

A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.

RESULTS

Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.

CONCLUSIONS

ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

Virtual and Augmented Reality Systems for Renal Interventions: A Systematic Review

PURPOSE

Many virtual and augmented reality systems have been proposed to support renal interventions. This paper reviews such systems employed in the treatment of renal cell carcinoma and renal stones.

METHODS

A systematic literature search was performed. Inclusion criteria were virtual and augmented reality systems for radical or partial nephrectomy and renal stone treatment, excluding systems solely developed or evaluated for training purposes.

RESULTS

In total, 52 research papers were identified and analyzed. Most of the identified literature (87%) deals with systems for renal cell carcinoma treatment. About 44% of the systems have already been employed in clinical practice, but only 20% in studies with ten or more patients. Main challenges remaining for future research include the consideration of organ movement and deformation, human factor issues, and the conduction of large clinical studies.

CONCLUSION

Augmented and virtual reality systems have the potential to improve safety and outcomes of renal interventions. In the last ten years, many technical advances have led to more sophisticated systems, which are already applied in clinical practice. Further research is required to cope with current limitations of virtual and augmented reality assistance in clinical environments.

[Minimally invasive surgery and robotic surgery: surgery 4.0?]

Surgery can only maintain its role in a highly competitive environment if results are continuously improved, accompanied by further reduction of the interventional trauma for patients and with justifiable costs. Significant impulse to achieve this goal was expected from minimally invasive surgery and, in particular, robotic surgery; however, a real breakthrough has not yet been achieved. Accordingly, the new strategic approach of cognitive surgery is required to optimize the provision of surgical treatment. A full scale integration of all modules utilized in the operating room (OR) into a comprehensive network and the development of systems with technical cognition are needed to upgrade the current technical environment passively controlled by the surgeon into an active collaborative support system (surgery 4.0). Only then can the true potential of minimally invasive surgery and robotic surgery be exploited.

[Intraoperative multidimensional visualization]

Modern intraoperative techniques of visualization are increasingly being applied in general and visceral surgery. The combination of diverse techniques provides the possibility of multidimensional intraoperative visualization of specific anatomical structures. Thus, it is possible to differentiate between normal tissue and tumor tissue and therefore exactly define tumor margins. The aim of intraoperative visualization of tissue that is to be resected and tissue that should be spared is to lead to a rational balance between oncological and functional results. Moreover, these techniques help to analyze the physiology and integrity of tissues. Using these methods surgeons are able to analyze tissue perfusion and oxygenation. However, to date it is not clear to what extent these imaging techniques are relevant in the clinical routine. The present manuscript reviews the relevant modern visualization techniques focusing on intraoperative computed tomography and magnetic resonance imaging as well as augmented reality, fluorescence imaging and optoacoustic imaging.

Intraoperative image-guided navigation system: development and applicability in 65 patients undergoing liver surgery

BACKGROUND

Image-guided systems have recently been introduced for their application in liver surgery. We aimed to identify and propose suitable indications for image-guided navigation systems in the domain of open oncologic liver surgery and, more specifically, in the setting of liver resection with and without microwave ablation.

METHOD

Retrospective analysis was conducted in patients undergoing liver resection with and without microwave ablation using an intraoperative image-guided stereotactic system during three stages of technological development (accuracy: 8.4 ± 4.4 mm in phase I and 8.4 ± 6.5 mm in phase II versus 4.5 ± 3.6 mm in phase III). It was evaluated, in which indications image-guided surgery was used according to the different stages of technical development.

RESULTS

Between 2009 and 2013, 65 patients underwent image-guided surgical treatment, resection alone (n = 38), ablation alone (n = 11), or a combination thereof (n = 16). With increasing accuracy of the system, image guidance was progressively used for atypical resections and combined microwave ablation and resection instead of formal liver resection (p < 0.0001).

CONCLUSION

Clinical application of image guidance is feasible, while its efficacy is subject to accuracy. The concept of image guidance has been shown to be increasingly efficient for selected indications in liver surgery. While accuracy of available technology is increasing pertaining to technological advancements, more and more previously untreatable scenarios such as multiple small, bilobar lesions and so-called vanishing lesions come within reach.