Artificial intelligence and robotics: a combination that is changing the operating room

Robotic surgery versus Laparoscopic surgery for anti-reflux and hiatal hernia surgery: a short-term outcomes and cost systematic literature review and meta-analysis

PURPOSE

The objective of this study is to compare the operative time, intraoperative complications, length of stay, readmission rates, overall complications, mortality, and cost associated with Robotic Surgery (RS) and Laparascopic Surgery (LS) in anti-reflux and hiatal hernia surgery.

METHODS

A comprehensive literature search was conducted using MEDLINE (via PubMed), Web of Science and Scopus databases. Studies comparing short-term outcomes and cost between RS and LS in patients with anti-reflux and hiatal hernia were included. Data on operative time, complications, length of stay, readmission rates, overall complications, mortality, and cost were extracted. Quality assessment of the included studies was performed using the MINORS scale.

RESULTS

Fourteen retrospective observational studies involving a total of 555,368 participants were included in the meta-analysis. The results showed no statistically significant difference in operative time, intraoperative complications, length of stay, readmission rates, overall complications, and mortality between RS and LS. However, LS was associated with lower costs compared to RS.

CONCLUSION

This systematic review and meta-analysis demonstrates that RS has non-inferior short-term outcomes in anti-reflux and hiatal hernia surgery, compared to LS. LS is more cost-effective, but RS offers potential benefits such as improved visualization and enhanced surgical techniques. Further research, including randomized controlled trials and long-term outcome studies, is needed to validate and refine these findings.

Generative artificial intelligence in surgery

Generative artificial intelligence is able to collect, extract, digest, and generate information in an understandable way for humans. As the first surgical applications of generative artificial intelligence are applied, this perspective paper aims to provide a comprehensive overview of current applications and future perspectives for the application of generative artificial intelligence in surgery, from preoperative planning to training. Generative artificial intelligence can be used before surgery for planning and decision support by extracting patient information and providing patients with information and simulation regarding the procedure. Intraoperatively, generative artificial intelligence can document data that is normally not captured as intraoperative adverse events or provide information to help decision-making. Postoperatively, GAIs can help with patient discharge and follow-up. The ability to provide real-time feedback and store it for later review is an important capability of GAIs. GAI applications are emerging as highly specialized, task-specific tools for tasks such as data extraction, synthesis, presentation, and communication within the realm of surgery. GAIs have the potential to play a pivotal role in facilitating interaction between surgeons and artificial intelligence.

Enhancing surgical planning for abdominal tumors in children through advanced 3D visualization techniques: a systematic review of future prospects

Introduction

Preoperative three-dimensional (3D) reconstruction using sectional imaging is increasingly used in challenging pediatric cases to aid in surgical planning. Many case series have described various teams' experiences, discussing feasibility and realism, while emphasizing the technological potential for children. Nonetheless, general knowledge on this topic remains limited compared to the broader research landscape. The aim of this review was to explore the current devices and new opportunities provided by preoperative Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI).

Methods

A systematic review was conducted to screen pediatric cases of abdominal and pelvic tumors with preoperative 3D reconstruction published between 2000 and 2023.

Discussion

Surgical planning was facilitated through virtual reconstruction or 3D printing. Virtual reconstruction of complex tumors enables precise delineation of solid masses, formulation of dissection plans, and suggests dedicated vessel ligation, optimizing tissue preservation. Vascular mapping is particularly relevant for liver surgery, large neuroblastoma with imaging-defined risk factors (IDRFs), and tumors encasing major vessels, such as complex median retroperitoneal malignant masses. 3D printing can facilitate specific tissue preservation, now accessible with minimally invasive procedures like partial nephrectomy. The latest advancements enable neural plexus reconstruction to guide surgical nerve sparing, for example, hypogastric nerve modelling, typically adjacent to large pelvic tumors. New insights will soon incorporate nerve plexus images into anatomical segmentation reconstructions, facilitated by non-irradiating imaging modalities like MRI.

Conclusion

Although not yet published in pediatric surgical procedures, the next anticipated advancement is augmented reality, enhancing real-time intraoperative guidance: the surgeon will use a robotic console overlaying functional and anatomical data onto a magnified surgical field, enhancing robotic precision in confined spaces.

Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.

What do nurses experience in communication when assisting in robotic surgery: an integrative literature review

BACKGROUND

Communication in surgery is integral to the fundamentals of perioperative nursing practice and patient safety. Research exploring team communication in robotic-assisted surgery (RAS) is evident in the literature but little attention has been focused on how the experiences of operating room nurses' communication affect safety, practice and patient care outcomes.

OBJECTIVE

To synthesise current evidence regarding communication during robotic-assisted surgery as experienced by registered nurses.

DESIGN

An integrative literature review informed by Whittemore and Knafl's (2005) methodology was used to conduct a rigorous analysis and synthesis of evidence.

METHODS

A comprehensive database search was conducted using PRISMA guidelines. CINAHL, Pubmed, PsychINFO and British Nursing Web of Science databases were searched using a Boolean strategy.

RESULTS

Twenty-five relevant papers were included in this literature review. Thematic analysis revealed two main themes with four related subthemes. The two main themes are: 'Adaptive operating room nursing in RAS' and 'RAS alters team dynamics'. The four subthemes are: 'Navigating disruptions in RAS', 'RAS heightens interdependence on team working', 'Augmented communicative workflow in RAS', and 'Professional empowerment to speak up'.

CONCLUSIONS

This integrative review identifies how current research largely focuses on communication in the wider OR team. However, current evidence lacks the input of nurses. Therefore, further evidence is needed to explore nurses' experiences to highlight their perspectives.

CLINICAL RELEVANCE

Robotics significantly benefit patients, and this review identifies different challenges that robotic-assisted surgery nurses encounter. A better understanding of the communication from the perspective of nurses is needed to guide future research, practice education, policy development and leadership/management.

Molecular image-guided surgery in gynaecological cancer: where do we stand?

PURPOSE

The aim of this review is to give an overview of the current status of molecular image-guided surgery in gynaecological malignancies, from both clinical and technological points of view.

METHODS

A narrative approach was taken to describe the relevant literature, focusing on clinical applications of molecular image-guided surgery in gynaecology, preoperative imaging as surgical roadmap, and intraoperative devices.

RESULTS

The most common clinical application in gynaecology is sentinel node biopsy (SNB). Other promising approaches are receptor-target modalities and occult lesion localisation. Preoperative SPECT/CT and PET/CT permit a roadmap for adequate surgical planning. Intraoperative detection modalities span from 1D probes to 2D portable cameras and 3D freehand imaging.

CONCLUSION

After successful application of radio-guided SNB and SPECT, innovation is leaning towards hybrid modalities, such as hybrid tracer and fusion of imaging approaches including SPECT/CT and PET/CT. Robotic surgery, as well as augmented reality and virtual reality techniques, is leading to application of these innovative technologies to the clinical setting, guiding surgeons towards a precise, personalised, and minimally invasive approach.

The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot's development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots-providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

Harvesting the Power of Artificial Intelligence for Surgery: Uses, Implications, and Ethical Considerations

Artificial intelligence is rapidly advancing, especially with the advent of ChatGPT technology, and its role in the world of medicine is expanding. Within surgery, AI has the capacity to improve efficiency and results in surgical treatments; however, it similarly has the potential to impose harm onto patients and undermine the role of medical providers. Its benefits may include improvements in surgical outcomes, spanning from enhanced pre-operative diagnostic capabilities to more refined intra-operative techniques, and long term patient experiences, by identifying and reducing complications. Nevertheless apprehensions revolve around laymen use potentially resulting in inappropriate therapeutic interventions, in addition to safety and ethical risks surrounding the use of patient data. Various strategies towards mitigating these harms must be considered, such as patient disclaimers and secondary review policies. While artificial intelligence brings exciting advancements to surgery, its integration must be cautiously monitored.

Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany

PURPOSE

The integration of artificial intelligence (AI) into surgical laparoscopy has shown promising results in recent years. This survey aims to investigate the inconveniences of current conventional laparoscopy and to evaluate the attitudes and desires of surgeons in Germany towards new AI-based laparoscopic systems.

METHODS

A 12-item web-based questionnaire was distributed to 38 German university hospitals as well as to a Germany-wide voluntary hospital association (CLINOTEL) consisting of 66 hospitals between July and November 2022.

RESULTS

A total of 202 questionnaires were completed. The majority of respondents (88.1%) stated that they needed one assistant during laparoscopy and rated the assistants' skillfulness as "very important" (39.6%) or "important" (49.5%). The most uncomfortable aspects of conventional laparoscopy were inappropriate camera movement (73.8%) and lens condensation (73.3%). Selected features that should be included in a new laparoscopic system were simple and intuitive maneuverability (81.2%), automatic de-fogging (80.7%), and self-cleaning of camera (77.2%). Furthermore, AI-based features were improvement of camera positioning (71.3%), visualization of anatomical landmarks (67.3%), image stabilization (66.8%), and tissue damage protection (59.4%). The reason for purchasing an AI-based system was to improve patient safety (86.1%); the reasonable price was €50.000-100.000 (34.2%), and it was expected to replace the existing assistants' workflow up to 25% (41.6%).

CONCLUSION

Simple and intuitive maneuverability with improved and image-stabilized camera guidance in combination with a lens cleaning system as well as AI-based augmentation of anatomical landmarks and tissue damage protection seem to be significant requirements for the further development of laparoscopic systems.

Improving Pharmacist-Led Pediatric Patient Education on Oral Chemotherapy at Home

Oral chemotherapy (OC) has been increasingly used in pediatric patients diagnosed with cancer, which is primarily managed in the outpatient setting. Different from adults, pediatric patients face unique challenges in administering these hazardous medications at home. Because of the complexity of pediatric pharmaceutical care and the hazardous nature of chemotherapy agents, comprehensive patient education is imperative to mitigate the potential safety risks associated with OC administration at home. Pharmacists play a vital role in patient education and medication consultations. However, the lack of practice guidelines and limited resources supporting OC counseling are noted. Additional barriers include insufficient knowledge and training on OC, which can be improved by continuing education. In a regional children's hospital, a comprehensive OC education checklist was developed for pediatric patients and their caregivers to standardize consultations led by pharmacists. An infographic OC handout was also formulated to improve patient knowledge and awareness. Moreover, innovative approaches such as using telepharmacy, smartphone applications, and artificial intelligence have been increasingly integrated into patient care, which can help optimize OC consultations for children and adolescents. Further studies are warranted to enhance oral chemotherapy education specifically tailored for pediatric patients in outpatient settings.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

Neuroendovascular Surgery Applications in Craniocervical Trauma

Cerebrovascular injuries resulting from blunt or penetrating trauma to the head and neck often lead to local hemorrhage and stroke. These injuries present with a wide range of manifestations, including carotid or vertebral artery dissection, pseudoaneurysm, occlusion, transection, arteriovenous fistula, carotid-cavernous fistula, epistaxis, venous sinus thrombosis, and subdural hematoma. A selective review of the literature from 1989 to 2023 was conducted to explore various neuroendovascular surgical techniques for craniocervical trauma. A PubMed search was performed using these terms: endovascular, trauma, dissection, blunt cerebrovascular injury, pseudoaneurysm, occlusion, transection, vasospasm, carotid-cavernous fistula, arteriovenous fistula, epistaxis, cerebral venous sinus thrombosis, subdural hematoma, and middle meningeal artery embolization. An increasing array of neuroendovascular procedures are currently available to treat these traumatic injuries. Coils, liquid embolics (onyx or n-butyl cyanoacrylate), and polyvinyl alcohol particles can be used to embolize lesions, while stents, mechanical thrombectomy employing stent-retrievers or aspiration catheters, and balloon occlusion tests and super selective angiography offer additional treatment options based on the specific case. Neuroendovascular techniques prove valuable when surgical options are limited, although comparative data with surgical techniques in trauma cases is limited. Further research is needed to assess the efficacy and outcomes associated with these interventions.

A Systematic Review of New Imaging Technologies for Robotic Prostatectomy: From Molecular Imaging to Augmented Reality

New imaging technologies play a pivotal role in the current management of patients with prostate cancer. Robotic assisted radical prostatectomy (RARP) is a standard of care for localized disease and through the already imaging-based console subject of research towards combinations of imaging technologies and RARP as well as their impact on surgical outcomes. Therefore, we aimed to provide a comprehensive analysis of the currently available literature for new imaging technologies for RARP. On 24 January 2023, we performed a systematic review of the current literature on Pubmed, Scopus and Web of Science according to the PRISMA guidelines and Oxford levels of evidence. A total of 46 studies were identified of which 19 studies focus on imaging of the primary tumor, 12 studies on the intraoperative tumor detection of lymph nodes and 15 studies on the training of surgeons. While the feasibility of combined approaches using new imaging technologies including MRI, PSMA-PET CT or intraoperatively applied radioactive and fluorescent dyes has been demonstrated, the prospective confirmation of improvements in surgical outcomes is currently ongoing.

In the Hands of a Robot, From the Operating Room to the Courtroom: The Medicolegal Considerations of Robotic Surgery

Robotic surgery has rapidly evolved as a groundbreaking field in medicine, revolutionizing surgical practices across various specialties. Despite its numerous benefits, the adoption of robotic surgery faces significant medicolegal challenges. This article delves into the underexplored legal implications of robotic surgery and identifies three distinct medicolegal problems. First, the lack of standardized training and credentialing for robotic surgery poses potential risks to patient safety and surgeon competence. Second, informed consent processes require additional considerations to ensure patients are fully aware of the technology's capabilities and potential risks. Finally, the issue of legal liability becomes complex due to the involvement of multiple stakeholders in the functioning of robotic systems. The article highlights the need for comprehensive guidelines, regulations, and training programs to navigate the medicolegal aspects of robotic surgery effectively, thereby unlocking its full potential for the future..

Toward Autonomous Pulmonary Artery Catheterization: A Learning-based Robotic Navigation System

Providing imaging during interventional treatments of cardiovascular diseases is challenging. Magnetic Resonance Imaging (MRI) has gained popularity as it is radiation-free and returns high resolution of soft tissue. However, the clinician has limited access to the patient, e.g., to their femoral artery, within the MRI scanner to accurately guide and manipulate an MR-compatible catheter. At the same time, communication will need to be maintained with a clinician, located in a separate control room, to provide the most appropriate image to the screen inside the MRI room. Hence, there is scope to explore the feasibility of how autonomous catheterization robots could support the steering of catheters along trajectories inside complex vessel anatomies.In this paper, we present a Learning from Demonstration based Gaussian Mixture Model for a robot trajectory optimisation during pulmonary artery catheterization. The optimisation algorithm is integrated into a 2 Degree-of-Freedom MR-compatible interventional robot allowing for continuous and simultaneous translation and rotation. Our methodology achieves autonomous navigation of the catheter tip from the inferior vena cava, through the right atrium and the right ventricle into the pulmonary artery where an interventions is performed. Our results show that our MR-compatible robot can follow an advancement trajectory generated by our Learning from Demonstration algorithm. Looking at the overall duration of the intervention, it can be concluded that procedures performed by the robot (teleoperated or autonomously) required significantly less time compared to manual hand-held procedures.

Artificial intelligence in theranostics of gastric cancer, a review

Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

Current State of Robotics in Interventional Radiology

As a relatively new specialty with a minimally invasive nature, the field of interventional radiology is rapidly growing. Although the application of robotic systems in this field shows great promise, such as with increased precision, accuracy, and safety, as well as reduced radiation dose and potential for teleoperated procedures, the progression of these technologies has been slow. This is partly due to the complex equipment with complicated setup procedures, the disruption to theatre flow, the high costs, as well as some device limitations, such as lack of haptic feedback. To further assess these robotic technologies, more evidence of their performance and cost-effectiveness is needed before their widespread adoption within the field. In this review, we summarise the current progress of robotic systems that have been investigated for use in vascular and non-vascular interventions.

Safe implementation of surgical innovation: a prospective registry of the Versius Robotic Surgical System

Objectives

To describe a new, international, prospective surgical registry developed to accompany the clinical implementation of the Versius Robotic Surgical System by accumulating real-world evidence of its safety and effectiveness.

Interventions

This robotic surgical system was introduced in 2019 for its first live-human case. With its introduction, cumulative database enrollment was initiated across several surgical specialties, with systematic data collection via a secure online platform.

Main outcome measures

Pre-operative data include diagnosis, planned procedure(s), characteristics (age, sex, body mass index and disease status) and surgical history. Peri-operative data include operative time, intra-operative blood loss and use of blood transfusion products, intra-operative complications, conversion to an alternative technique, return to the operating room prior to discharge and length of hospital stay. Complications and mortality within 90 days of surgery are also recorded.

Results

The data collected in the registry are analyzed as comparative performance metrics, by meta-analyses or by individual surgeon performance using control method analysis. Continual monitoring of key performance indicators, using various types of analyses and outputs within the registry, have provided meaningful insights that help institutions, teams and individual surgeons to perform most effectively and ensure optimal patient safety.

Conclusions

Harnessing the power of large-scale, real-world registry data for routine surveillance of device performance in live-human surgery from first use will enhance the safety and efficacy outcomes of innovative surgical techniques. Data are crucial to driving the evolution of robot-assisted minimal access surgery while minimizing risk to patients.

Trial registration number

CTRI/2019/02/017872.

The use and future perspective of Artificial Intelligence-A survey among German surgeons

Purpose

Clinical abundance of artificial intelligence has increased significantly in the last decade. This survey aims to provide an overview of the current state of knowledge and acceptance of AI applications among surgeons in Germany.

Methods

A total of 357 surgeons from German university hospitals, academic teaching hospitals and private practices were contacted by e-mail and asked to participate in the anonymous survey.

Results

A total of 147 physicians completed the survey. The majority of respondents (n = 85, 52.8%) stated that they were familiar with AI applications in medicine. Personal knowledge was self-rated as average (n = 67, 41.6%) or rudimentary (n = 60, 37.3%) by the majority of participants. On the basis of various application scenarios, it became apparent that the respondents have different demands on AI applications in the area of "diagnosis confirmation" as compared to the area of "therapy decision." For the latter category, the requirements in terms of the error level are significantly higher and more respondents view their application in medical practice rather critically. Accordingly, most of the participants hope that AI systems will primarily improve diagnosis confirmation, while they see their ethical and legal problems with regard to liability as the main obstacle to extensive clinical application.

Conclusion

German surgeons are in principle positively disposed toward AI applications. However, many surgeons see a deficit in their own knowledge and in the implementation of AI applications in their own professional environment. Accordingly, medical education programs targeting both medical students and healthcare professionals should convey basic knowledge about the development and clinical implementation process of AI applications in different medical fields, including surgery.

Meeting sustainable development goals via robotics and autonomous systems

Robotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities.

A horizon scan was used to explore possible impacts of robotics and automated systems on achieving the UN Sustainable Development Goals. Positive effects are likely. Iterative regulatory processes and continued dialogue could help avoid environmental damages and increases in inequality.

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: Comparative Analysis With Guy’s Stone Score and the S.T.O.N.E Score System

Purpose: The aim of the study was to use machine learning methods (MLMs) to predict the stone-free status after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy’s stone score and the S.T.O.N.E score system.

Materials and Methods: Data from 222 patients (90 females, 41%) who underwent PCNL at our center were used. Twenty-six parameters, including individual variables, renal and stone factors, and surgical factors were used as input data for MLMs. We evaluated the efficacy of four different techniques: Lasso-logistic (LL), random forest (RF), support vector machine (SVM), and Naive Bayes. The model performance was evaluated using the area under the curve (AUC) and compared with that of Guy’s stone score and the S.T.O.N.E score system.

Results: The overall stone-free rate was 50% (111/222). To predict the stone-free status, all receiver operating characteristic curves of the four MLMs were above the curve for Guy’s stone score. The AUCs of LL, RF, SVM, and Naive Bayes were 0.879, 0.803, 0.818, and 0.803, respectively. These values were higher than the AUC of Guy’s score system, 0.800. The accuracies of the MLMs (0.803% to 0.818%) were also superior to the S.T.O.N.E score system (0.788%). Among the MLMs, Lasso-logistic showed the most favorable AUC.

Conclusion: Machine learning methods can predict the stone-free rate with AUCs not inferior to those of Guy’s stone score and the S.T.O.N.E score system.

Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

Artificial intelligence in thoracic surgery: a narrative review

Objective

The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction.

Background

Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway.

Methods

We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021.

Conclusion

Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice.

Keywords

Artificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine

How molecular imaging will enable robotic precision surgery : The role of artificial intelligence, augmented reality, and navigation

Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities.

A systematic review on artificial intelligence in robot-assisted surgery

BACKGROUND

Despite the extensive published literature on the significant potential of artificial intelligence (AI) there are no reports on its efficacy in improving patient safety in robot-assisted surgery (RAS). The purposes of this work are to systematically review the published literature on AI in RAS, and to identify and discuss current limitations and challenges.

MATERIALS AND METHODS

A literature search was conducted on PubMed, Web of Science, Scopus, and IEEExplore according to PRISMA 2020 statement. Eligible articles were peer-review studies published in English language from January 1, 2016 to December 31, 2020. Amstar 2 was used for quality assessment. Risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data of the studies were visually presented in tables using SPIDER tool.

RESULTS

Thirty-five publications, representing 3436 patients, met the search criteria and were included in the analysis. The selected reports concern: motion analysis (n = 17), urology (n = 12), gynecology (n = 1), other specialties (n = 1), training (n = 3), and tissue retraction (n = 1). Precision for surgical tools detection varied from 76.0% to 90.6%. Mean absolute error on prediction of urinary continence after robot-assisted radical prostatectomy (RARP) ranged from 85.9 to 134.7 days. Accuracy on prediction of length of stay after RARP was 88.5%. Accuracy on recognition of the next surgical task during robot-assisted partial nephrectomy (RAPN) achieved 75.7%.

CONCLUSION

The reviewed studies were of low quality. The findings are limited by the small size of the datasets. Comparison between studies on the same topic was restricted due to algorithms and datasets heterogeneity. There is no proof that currently AI can identify the critical tasks of RAS operations, which determine patient outcome. There is an urgent need for studies on large datasets and external validation of the AI algorithms used. Furthermore, the results should be transparent and meaningful to surgeons, enabling them to inform patients in layman's words.

REGISTRATION

Review Registry Unique Identifying Number: reviewregistry1225.

Robotic Surgery: At the Crossroads of a Data Explosion

BACKGROUND

For the last 20 years, controversies in robotic surgery focused on cost reduction, development of new platforms and technologies, creation and validation of curriculum and virtual simulators, and conduction of randomized clinical trials to determine the best applications of robotics [Leal Ghezzi and Campos in World J Surg 40:2550-2557, 2016].

METHODS

This review explores the robotic systems which are currently indicated for use or development in gastrointestinal/abdominal surgery. These systems are reviewed and analyzed for clinical impact in these areas. In a MEDLINE search of articles with the search terms abdominal, gastrointestinal, review and robotic surgery, a total of 4306 total articles as of 2021 were assessed. Publicly available information, highest cited articles and reviews were assessed by the authors to determine the most significant regarding clinical outcomes.

RESULTS

Despite this increased number of articles related to robotic surgery, ongoing controversies have led to limitation in the use of current and future robotic surgery platforms [Connelly et al. in J Robotic Surg 14:155-165, 2020]. Newer robotic platforms have limited studies or analysis that would allow meaningful definite conclusions. A multitude of new scenarios are possible due to this limited information.

CONCLUSION

Robotic surgery is in evolution to a larger conceptual field of computationally enhanced surgery (CES). Various terms have been used in the literature including computer-assisted surgery or digital Surgery [Ranev and Teixeira in Surg Clin North Am 100:209-218, 2020]. With the growth of technological changes inherent in CES, the ability to validate these improvements in outcomes will require new metrics and analytic tools. This learning feedback and metric analysis will generate the new opportunities in simulation, training and application [Julian and Smith in Int J Med Robot 15:e2037, 2019].

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.

Artificial intelligence and simulation in urology

INTRODUCTION AND OBJECTIVE

Artificial intelligence (AI) is in full development and its implementation in medicine has led to an improvement in clinical and surgical practice. One of its multiple applications is surgical training, with the creation of programs that allow avoiding complications and risks for the patient. The aim of this article is to analyze the advantages of AI applied to surgical training in urology.

MATERIAL AND METHODS

A literary research is carried out to identify articles published in English regarding AI applied to medicine, especially in surgery and the acquisition of surgical skills.

RESULTS

Surgical training has evolved over time thanks to AI. A model for surgical learning where skills are acquired in a progressive way while avoiding complications to the patient, has been created. The use of simulators allows a progressive learning, providing trainees with procedures that increase in number and complexity. On the other hand, AI is used in imaging tests for surgical or treatment planning.

CONCLUSION

Currently, the use of AI in daily clinical practice has led to progress in medicine, specifically in surgical training.

The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory

BACKGROUND

Machine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians' practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians' competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician-patient relationship might be affected by the employment of ML_CDSS.

MAIN TEXT

Professionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient's concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.

CONCLUSIONS

Using ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient's personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient's concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making.

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives

PURPOSE OF REVIEW

As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of skills to ensure maximum patient safety. This article reviews advances in the use of machine learning and artificial intelligence for improvements of surgical skills in urology.

RECENT FINDINGS

Six studies have been published, which met the inclusion criteria. All articles assessed the application of artificial intelligence in improving surgical training. Different approaches were taken, such as using machine learning to identify and classify suturing gestures, creating automated objective evaluation reports, and determining surgical technical skill levels to predict clinical outcomes. The articles illustrated the continuously growing role of artificial intelligence to address the difficulties currently present in evaluating urological surgical skills.

SUMMARY

Artificial intelligence allows us to efficiently analyze the surmounting data related to surgical training and use it to come to conclusions that normally would require human intelligence. Although these metrics have been shown to predict surgeon expertise and surgical outcomes, evidence is still scarce regarding their ability to directly improve patient outcomes. Considering this, current active research is growing on the topic of deep learning-based computer vision to provide automated metrics needed for real-time surgeon feedback.

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.

Artificial intelligence in gastric cancer: a translational narrative review

Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application. There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies. It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.

Usability of Indocyanine Green in Robot-Assisted Hepatic Surgery

Recent developments in robotic surgery have led to an increasing number of robot-assisted hepatobiliary procedures. However, a limitation of robotic surgery is the missing haptic feedback. The fluorescent dye indocyanine green (ICG) may help in this context, which accumulates in hepatocellular cancers and around hepatic metastasis. ICG accumulation may be visualized by a near-infrared camera integrated into some robotic systems, helping to perform surgery more accurately. We aimed to test the feasibility of preoperative ICG application and its intraoperative use in patients suffering from hepatocellular carcinoma and metastasis of colorectal cancer, but also of other origins. In a single-arm, single-center feasibility study, we tested preoperative ICG application and its intraoperative use in patients undergoing robot-assisted hepatic resections. Twenty patients were included in the final analysis. ICG staining helped in most cases by detecting a clear lesion or additional metastases or when performing an R0 resection. However, it has limitations if applied too late before surgery and in patients suffering from severe liver cirrhosis. ICG staining may serve as a beneficial intraoperative aid in patients undergoing robot-assisted hepatic surgery. Dose and time of application and standardized fluorescence intensity need to be further determined.

Automatic speech recognition in the operating room - An essential contemporary tool or a redundant gadget? A survey evaluation among physicians in form of a qualitative study

Introduction

For decades, automatic speech recognition (ASR) has been the subject of research and its range of applications broadened. Presently, ASR among physicians is mainly used to convert speech into text but not to implement instructions in the operating room (OR). This study aimed to evaluate physicians of different surgical professions on their personal experience and posture towards ASR.

Methods

A 16-item survey was distributed electronically to hospitals and outpatient clinics in southern Germany addressing physicians on the potential applications of ASR in the OR.

Results

The survey was responded by 185 of 2693 physicians (response rate: 6.9%) with a mean age of 41.8 ± 9.8 years. ASR is desirable in the OR regardless of the field of speciality (93.7%). While only 2.7% have used ASR, 87.9% evaluate its future potential as high. 91.0% of those working in a university hospital would consider testing ASR, while 67.5% of those in non-university hospitals and practices (p = 0.001). 90.1% of responders of strictly surgical specialities see potential in ASR while 73.7% in non-surgical specialities evaluate its future potential as high (p = 0.01). 58.3% of those over the age of 60 consider the use of ASR without a headset to be imaginable, while 96.3% among those under the age of 60. There were no statistically significant differences regarding sex and professional position.

Conclusion

Foreseeably, ASR is anticipated to be integrated into ORs and valued at a high market potential. Our study provides information about physicians’ individual preferences from various surgical disciplines regarding ASR.

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ASR is a desirable tool in the OR regardless of the field of speciality.

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While 90.1% interviewees from surgical specialities see potential in ASR and 73.7% from non-surgical specialities assess its future potential as high.

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There were no statistically significant differences regarding sex and professional position.

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For an ASR system to be implemented in the OR, it needs to be sophisticated and updatable since there are still several technical requirements to be fulfilled.

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Foreseeably, ASR is anticipated to be integrated into the OR and valued at a high market potential.

Artificial intelligence in gastric cancer: a systematic review

OBJECTIVE

This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer.

METHODS

A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms "artificial intelligence" and "gastric cancer" were used to search for the publications.

RESULTS

A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance.

CONCLUSIONS

In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.