Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical ...adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human-machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective.
5G in Healthcare: From COVID-19 to Future Challenges Moglia, Andrea; Georgiou, Konstantinos; Marinov, Blagoi ...
IEEE journal of biomedical and health informatics,
08/2022, Letnik:
26, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 ...from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients' data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.
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.
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.
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%.
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.
Review Registry Unique Identifying Number: reviewregistry1225.
•Several limitations and technical issues were found in the reviewed published studies.•There is no proof that currently AI can recognize critical tasks of RAS operations.•Studies on large datasets together with external validation of AI algorithms are needed.•More transparent results are needed, which are meaningful to surgeons in future publications on AI.•A glossary and an appendix for better understanding of the AI terms have been added.
Background Preoperative simulation warm-up has been shown to improve performance and reduce errors in novice and experienced surgeons, yet existing studies have only investigated conventional ...laparoscopy. We hypothesized that a brief virtual reality (VR) robotic warm-up would enhance robotic task performance and reduce errors. Study Design In a 2-center randomized trial, 51 residents and experienced minimally invasive surgery faculty in General Surgery, Urology, and Gynecology underwent a validated robotic surgery proficiency curriculum on a VR robotic simulator and on the da Vinci surgical robot (Intuitive Surgical Inc). Once they successfully achieved performance benchmarks, surgeons were randomized to either receive a 3- to 5-minute VR simulator warm-up or read a leisure book for 10 minutes before performing similar and dissimilar (intracorporeal suturing) robotic surgery tasks. The primary outcomes compared were task time, tool path length, economy of motion, technical, and cognitive errors. Results Task time (−29.29 seconds, p = 0.001; 95% CI, −47.03 to −11.56), path length (−79.87 mm; p = 0.014; 95% CI, −144.48 to −15.25), and cognitive errors were reduced in the warm-up group compared with the control group for similar tasks. Global technical errors in intracorporeal suturing (0.32; p = 0.020; 95% CI, 0.06−0.59) were reduced after the dissimilar VR task. When surgeons were stratified by earlier robotic and laparoscopic clinical experience, the more experienced surgeons (n = 17) demonstrated significant improvements from warm-up in task time (−53.5 seconds; p = 0.001; 95% CI, −83.9 to −23.0) and economy of motion (0.63 mm/s; p = 0.007; 95% CI, 0.18–1.09), and improvement in these metrics was not statistically significantly appreciated in the less-experienced cohort (n = 34). Conclusions We observed significant performance improvement and error reduction rates among surgeons of varying experience after VR warm-up for basic robotic surgery tasks. In addition, the VR warm-up reduced errors on a more complex task (robotic suturing), suggesting the generalizability of the warm-up.
The uses of robotics in surgery were hypothesized as far back as 1967, but it took nearly 30 years and the nation's largest agency, the Department of Defense, in conjunction with innovative startups ...and established research agencies to complete the first fully functional multipurpose surgical robot. Currently, the most prominently available multipurpose robotic surgery system with US Food and Drug Administration approval is Intuitive Surgical Inc.'s da Vinci Surgical System, which is found in operating rooms across the globe. Although now ubiquitous for minimally invasive surgery, early surgical robot prototypes were specialty focused. Originally, multipurpose robotic systems were intended for long-distance trauma surgery in battlefield settings. While there were impressive feats of telesurgery, the marketable focus has veered from this goal. Initially developed through SRI International and Defense Advanced Research Projects Agency, surgical robotics reached private industry through two major competitors, who later merged.
A thorough search of PubMed, Clinical Key, EBSCO, Ovid, ProQuest, and industry manufacturers' websites yielded 62 relevant articles, of which 51 were evaluated in this review.
We analyzed the literature and referred to primary sources by conducting interviews with present and historical leaders in the field to yield a detailed chronology of surgical robotics development. As minimally invasive robotic procedures are becoming the standard of care, it is crucial to comprehensively document their historical context and importance as an emerging and evolving discipline.
Background
The literature on artificial intelligence (AI) in surgery has advanced rapidly during the past few years. However, the published studies on AI are mostly reported by computer scientists ...using their own jargon which is unfamiliar to surgeons.
Methods
A literature search was conducted in using PubMed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. The primary outcome of this review is to provide a glossary with definitions of the commonly used AI terms in surgery to improve their understanding by surgeons.
Results
One hundred ninety-five studies were included in this review, and 38 AI terms related to surgery were retrieved. Convolutional neural networks were the most frequently culled term by the search, accounting for 74 studies on AI in surgery, followed by classification task (
n
= 62), artificial neural networks (
n
= 53), and regression (
n
= 49). Then, the most frequent expressions were supervised learning (reported in 24 articles), support vector machine (SVM) in 21, and logistic regression in 16. The rest of the 38 terms was seldom mentioned.
Conclusions
The proposed glossary can be used by several stakeholders. First and foremost, by residents and attending consultant surgeons, both having to understand the fundamentals of AI when reading such articles. Secondly, junior researchers at the start of their career in Surgical Data Science and thirdly experts working in the regulatory sections of companies involved in the AI Business Software as a Medical Device (SaMD) preparing documents for submission to the Food and Drug Administration (FDA) or other agencies for approval.
Simulation-based surgical skills training addresses several concerns associated with the traditional apprenticeship model, including patient safety, efficient acquisition of complex skills, and cost. ...The surgical specialties already recognize the advantages of surgical training using simulation, and simulation-based methods are appearing in surgical education and assessment for board certification. The necessity of simulation-based methods in surgical education along with valid, objective, standardized techniques for measuring learned skills using simulators has become apparent. The most commonly used surgical skill measurement techniques in simulation-based training include questionnaires and post-training surveys, objective structured assessment of technical skills and global rating scale of performance scoring systems, structured assessments using video recording, and motion tracking software. The literature shows that the application of many of these techniques varies based on investigator preference and the convenience of the technique. As simulators become more accepted as a teaching tool, techniques to measure skill proficiencies will need to be standardized nationally and internationally.
5G Use in Healthcare: The Future is Present Georgiou, Konstantinos E; Georgiou, Evangelos; Satava, Richard M
Journal of the Society of Laparoendoscopic Surgeons,
10/2021, Letnik:
25, Številka:
4
Journal Article
Recenzirano
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Most healthcare providers are unaware of the extraordinary opportunities for implementation in healthcare which can be enabled by 5G wireless networks. 5G created enormous opportunities for a myriad ...of new technologies, resulting in an integrated through 5G 'ecosystem'. Although the new opportunities in healthcare are immense, medicine is slow to change, as manifest by the paucity of new, innovative applications based upon this ecosystem. Thus, emerges the need to "avoid technology surprise" - both laparoscopic and robotic assisted minimally invasive surgery were delayed for years because the surgical community was either unaware or unaccepting of a new technology.
PubMed (Medline) and Scopus (Elsevier) databases were searched and all published studies regarding clinical applications of 5G were retrieved. From a total of 40 articles, 13 were finally included in our review.
The important transformational properties of 5G communications and other innovative technologies are described and compared to healthcare needs, looking for opportunities, limitations, and challenges to implementation of 5G and the ecosystem it has spawned. Furthermore, the needs in the clinical applications, education and research in medicine and surgery, in addition to the administrative infrastructure are addressed. Additionally, we explore the nontechnical challenges, that either support or oppose this new healthcare renovation. Based upon proven advantages of these innovative technologies, current scientific evidence is analyzed for future trends for the transformation of healthcare. By providing awareness of these opportunities and their advantages for patients, it will be possible to decrease the prolonged timeframe for acceptance and implementation for patients.
Evaluating the quality of a scientific article has proven to be an elusive task. The most widely used bibliometric value currently used for this purpose, the journal impact factor, was not originally ...designed to determine the quality of research in a scientific article. Nevertheless, it has morphed into a surrogate to delineate the quality of a journal and even to represent the quality of individual articles in that that journal. Early 21
century advances in computer technology have seen an explosive revolution in scientific publication that have included open access, online publication, and world-wide accessibility to these publications. These developments have made it obvious that more sophisticated tools are required to delimit the quality of material present in the scientific literature. Usage data, which is measured as the number of full-text downloads of a specific article, is just one new method to evaluate the source of the vast material available that can be leveraged to more fully evaluate the merit of scientific literature.
As the role of robot-assisted surgery continues to expand, development of standardised and validated training programmes is becoming increasingly important.
To provide guidance on an optimised ...“train-the-trainer” (TTT) structured educational programme for surgical trainers, in which delegates learn a standardised approach to training candidates in skill acquisition. We aim to describe a TTT course for robotic surgery based on the current published literature and to define the key elements within a TTT course by seeking consensus from an expert committee formed of key opinion leaders in training.
The project was carried out in phases: a systematic review of the current evidence was conducted, a face-to-face meeting was held in Philadelphia, and then an initial survey was created based on the current literature and expert opinion and sent to the committee. Thirty-two experts in training, including clinicians, academics, and industry, contributed to the Delphi process. The Delphi process underwent three rounds of survey in total. Additions to the second- and third-round surveys were formulated based on the answers and comments from the previous rounds. Consensus opinion was defined as ≥80% agreement.
There was 100% consensus that there was a need for a standardized TTT course in robotic surgery. A consensus was reached in multiple areas, including the following: (1) definitions and terminologies, (2) qualifications to attend, (3) course objectives, (4) precourse considerations, (5) requirement of e-learning, (6) theory and course content, and (7) measurement of outcomes and performance level verification. The resulting formulated curriculum showed good internal consistency among experts, with a Cronbach alpha of 0.90.
Using the Delphi methodology, we achieved an international consensus among experts to develop and reach content validation for a standardised TTT curriculum for robotic surgery training. This defined content lays the foundation for developing a proficiency-based progression model for trainers in robotic surgery. This TTT curriculum will require further validation.
As the role of robot-assisted surgery continues to expand, development of standardised and validated training programmes is becoming increasingly important. There is currently a lack of high-level evidence on how best to train trainers in robot-assisted surgery. We report a consensus view on a standardised “train-the trainer” curriculum focused on robotic surgery. It was formulated by training experts from the USA and Europe, combining current evidence for training with experts’ knowledge of surgical training.
There is currently a lack of high-level evidence on how best to train trainers in robot-assisted surgery. We report a Delphi process consensus view formulated by US and European training experts on a standardised “train-the trainer” curriculum for robotic surgery.
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