Study DesignA narrative review of the current literature on the application of Computer-Assisted Navigation (CAN) in cervical spine surgeries.ObjectiveTo analyze the perioperative integration, types of CAN systems, technical considerations, and clinical applications of CAN in cervical spine surgeries, as well as to assess the associated complications and potential strategies to minimize these risks.MethodsA comprehensive review of published studies between 2015 and 2024 was conducted to evaluate the usage, benefits, and challenges of CAN in cervical spine surgeries. The review covered perioperative integration, system types, complications, and emerging technologies, including augmented reality (AR) and robotics.ResultsThe use of CAN in cervical spine surgeries provides improved accuracy in screw placement and reduced neurovascular complications. However, the review identified several limitations, such as a steep learning curve, cost considerations, and potential inaccuracies related to cervical spine mobility.ConclusionsCAN offers significant benefits in cervical spine surgeries, including enhanced precision and reduced complications. Despite the current limitations, advancements in AR and robotics hold promise for improving the safety and effectiveness of CAN in cervical procedures. The future focus should be on overcoming the existing challenges to increase the adoption of CAN in cervical spine surgeries.

Embodied AI (E-AI) in the form of intelligent surgical robotics and other agents is calling for data platforms to facilitate its development and deployment. In this work, we present a cross-platform multimodal data recording and streaming software, MUTUAL, successfully deployed on two clinical studies, along with its ROS 2 distributed adaptation, MUTUAL-ROS 2. We describe and compare the two implementations of MUTUAL through their recording performance under different settings. MUTUAL offers robust recording performance at target configurations for multiple modalities, including video, audio, and live expert commentary. While this recording performance is not matched by MUTUAL-ROS 2, we demonstrate its advantages related to real-time streaming capabilities for AI inference and more horizontal scalability, key aspects for E-AI systems in the operating room. Our findings demonstrate that the baseline MUTUAL is well-suited for data curation and offline analysis, whereas MUTUAL-ROS 2, should match the recording reliability of the baseline system under a fully distributed manner where modalities are handled independently by edge computing devices. These insights are critical for advancing the integration of E-AI in surgical practice, ensuring that data infrastructure can support both robust recording and real-time processing needs.

Minimally invasive internal fixation is the preferred treatment option for femoral shaft fractures. However, there are problems such as invisibility, inaccuracy and instability in the process of plate implantation.

In this paper, a dual-mode robot-assisted plate implantation method was proposed by combining a starting point determination algorithm, motion capture, deep learning and robotics.

The neural network model planned the plate implantation trajectory according to patient's condition. Then, the advantages of high stability and high precision of the robotic arm were used for plate implantation.

The trend and fluctuation of the plate implantation trajectories obtained using this method met clinical requirements. Furthermore, the robotic arm implantation process was safe.

The rise of high-definition imaging and robotic surgery has independently been associated with improved postoperative outcomes. However, steep learning curves and finite human cognitive ability limit the facility in imaging interpretation and interaction with the robotic surgery console interfaces. This review presents innovative ways in which artificial intelligence integrates preoperative imaging and surgery to help overcome these limitations and to further advance robotic operations.

PubMed was queried for "artificial intelligence," "machine learning," and "robotic surgery." From the 182 publications in English, a further in-depth review of the cited literature was performed.

Artificial intelligence boasts efficiency and proclivity for large amounts of unwieldy and unstructured data. Its wide adoption has significant practice-changing implications throughout the perioperative period. Assessment of preoperative imaging can augment preoperative surgeon knowledge by accessing pathology data that have been traditionally only available postoperatively through analysis of preoperative imaging. Intraoperatively, the interaction of artificial intelligence with augmented reality through the dynamic overlay of preoperative anatomical knowledge atop the robotic operative field can outline safe dissection planes, helping surgeons make critical real-time intraoperative decisions. Finally, semi-independent artificial intelligence-assisted robotic operations may one day be performed by artificial intelligence with limited human intervention.

As artificial intelligence has allowed machines to think and problem-solve like humans, it promises further advancement of existing technologies and a revolution of individualized patient care. Further research and ethical precautions are necessary before the full implementation of artificial intelligence in robotic surgery.

Robotic technology is an important tool in surgical innovation, with robots increasingly being used in the clinical setting. Robots can be used to enhance accuracy, perform remote actions, or to automate tasks. One such surgical task is suturing, a repetitive, fundamental component of surgery that can be tedious and time consuming. Suturing is a promising automation target because of its ubiquity, repetitive nature, and defined constraints. This systematic review examines research to date on autonomous suturing.

A systematic review of the literature focused on autonomous suturing was conducted in accordance with PRISMA guidelines.

6850 articles were identified by searching PubMed, Embase, Compendex, and Inspec. Duplicates and non-English articles were removed. 4389 articles were screened and 4305 were excluded. Of the 84 remaining, 43 articles did not meet criteria, leaving 41 articles for final review. Among these, 34 (81%) were published after 2014. 31 (76%) were published in an engineering journal9 in a robotics journal, and 1 in a medical journal. The great majority of articles (33, 80%) did not have a specific clinical specialty focus, whereas 6 (15%) were focused on applications in MIS/laparoscopic surgery and 2 (5%) on applications in ophthalmology. Several suturing subtasks were identified, including knot tying, suture passing/needle insertion, needle passing, needle and suture grasping, needle tracking/kinesthesia, suture thread detection, suture needle shape production, instrument assignment, and suture accuracy. 14 articles were considered multi-component because they referred to several previously mentioned subtasks.

In this systematic review exploring research to date on autonomous suturing, 41 articles demonstrated significant progress in robotic suturing. This summary revealed significant heterogeneity of work, with authors focused on different aspects of suturing and a multitude of engineering problems. The review demonstrates increasing academic and commercial interest in surgical automation, with significant technological advances toward feasibility.

In recent decades, there has been a rising trend of spinal surgical interventional techniques, especially Minimally Invasive Spine Surgery (MIS), to improve the quality of life in an effective and safe manner. However, MIS techniques tend to be difficult to adapt and are associated with an increased risk of radiation exposure. This led to the development of 'computer-assisted surgery' in 1983, which integrated CT images into spinal procedures evolving into the present day robotic-assisted spine surgery. The authors aim to review the development of spine surgeries and provide an overview of the benefits offered. It includes all the comparative studies available to date.

The manuscript has been prepared as per "SANRA-a scale for the quality assessment of narrative review articles". The authors searched Pubmed, Embase, and Scopus using the terms "(((((Robotics) OR (Navigation)) OR (computer assisted)) OR (3D navigation)) OR (Freehand)) OR (O-Arm)) AND (spine surgery)" and 68 articles were included for analysis excluding review articles, meta-analyses, or systematic literature.

The authors noted that 49 out of 68 studies showed increased precision of pedicle screw insertion, 10 out of 19 studies show decreased radiation exposure, 13 studies noted decreased operative time, 4 out of 8 studies showed reduced hospital stay and significant reduction in rates of infections, neurological deficits, the need for revision surgeries, and rates of radiological ASD, with computer-assisted techniques.

Computer-assisted surgeries have better accuracy of pedicle screw insertion, decreased blood loss and operative time, reduced radiation exposure, improved functional outcomes, and lesser complications.

Applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers. Multidisciplinary teams will be needed to address these challenges and to build AI systems that are relevant, equitable, and dynamic.

Surgical robots were invented in the 1980s, and since then, robotic-assisted surgery has become commonplace. In the field of spine surgery, robotic assistance is utilized mainly to place pedicle screws, and multiple studies have demonstrated that robots can increase the accuracy of screw placement and reduce radiation exposure to the patient and the surgeon. However, this may be at the cost of longer operative times, complications, and the risk of errors in mapping the patient's anatomy.

Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing "surgeon-assist"-some forms of autonomy-has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lanekeeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends.

Hepatic surgery is a rapidly expanding component of abdominal surgery and is performed for a wide range of indications. The introduction of laparoscopic cholecystectomy in 1987 was a major change in abdominal surgery. Laparoscopic surgery was widely and rapidly adopted throughout the world for cholecystectomy initially and then applied to a variety of other procedures. Laparoscopic surgery became regularly applied to hepatic surgery, including segmental and major resections as well as organ donation. Many operations progressed from open surgery to laparoscopy to robot-assisted surgery, including colon resection, pancreatectomy, splenectomy thyroidectomy, adrenalectomy, prostatectomy, gastrectomy, and others. It is difficult to prove a data-based benefit using robot-assisted surgery, although laparoscopic and robot-assisted surgery of the liver are not inferior regarding major outcomes. When laparoscopic surgery initially became popular, many had concerns about its use to treat malignancies. Robot-assisted surgery is being used to treat a variety of benign and malignant conditions, and studies have shown no deterioration in outcomes. Robot-assisted surgery for the treatment of malignancies has become accepted and is now being used at more centers. The outcomes after robot-assisted surgery depend on its use at specialized centers, the surgeon's personal experience backed up by extensive training and maintenance of international registries. Robot-assisted hepatic surgery has been shown to be associated with slightly less intraoperative blood loss and shorter hospital lengths of stay compared to open surgery. Oncologic outcomes have been maintained, and some studies show higher rates of R0 resections. Patients who need surgery for liver lesions should identify a surgeon they trust and should not be concerned with the specific operative approach used. The growth of robot-assisted surgery of the liver has occurred in a stepwise approach which is very different from the frenzy that was seen with the introduction of laparoscopic cholecystectomy. This approach allowed the identification of areas for improvement, many of which are at the nexus of engineering and medicine. Further improvements in robot-assisted surgery depend on the combined efforts of engineers and surgeons.

From traditional open surgery to laparoscopic surgery and robot-assisted surgery, advances in robotics, machine learning, and imaging are pushing the surgical approach to-wards better clinical outcomes. Pre-clinical and clinical evidence suggests that automation may standardise techniques, increase efficiency, and reduce clinical complications.

A PRISMA-guided search was conducted across PubMed and OVID.

Of the 89 screened articles, 51 met the inclusion criteria, with 10 included in the final review. Automatic data segmentation, trajectory planning, intra-operative registration, trajectory drilling, and soft tissue robotic surgery were discussed.

Although automated surgical systems remain conceptual, several research groups have developed supervised autonomous robotic surgical systems with increasing consideration for ethico-legal issues for automation. Automation paves the way for precision surgery and improved safety and opens new possibilities for deploying more robust artificial intelligence models, better imaging modalities and robotics to improve clinical outcomes.

An autonomous robotic laparoscopic surgical technique is capable of tracking tissue motion and offers consistency in suturing for the anastomosis of the small bowel.

Autonomous robotic surgery has the potential to provide efficacy, safety, and consistency independent of individual surgeon's skill and experience. Autonomous anastomosis is a challenging soft-tissue surgery task because it requires intricate imaging, tissue tracking, and surgical planning techniques, as well as a precise execution via highly adaptable control strategies often in unstructured and deformable environments. In the laparoscopic setting, such surgeries are even more challenging because of the need for high maneuverability and repeatability under motion and vision constraints. Here we describe an enhanced autonomous strategy for laparoscopic soft tissue surgery and demonstrate robotic laparoscopic small bowel anastomosis in phantom and in vivo intestinal tissues. This enhanced autonomous strategy allows the operator to select among autonomously generated surgical plans and the robot executes a wide range of tasks independently. We then use our enhanced autonomous strategy to perform in vivo autonomous robotic laparoscopic surgery for intestinal anastomosis on porcine models over a 1-week survival period. We compared the anastomosis quality criteria-including needle placement corrections, suture spacing, suture bite size, completion time, lumen patency, and leak pressure-of the developed autonomous system, manual laparoscopic surgery, and robot-assisted surgery (RAS). Data from a phantom model indicate that our system outperforms expert surgeons' manual technique and RAS technique in terms of consistency and accuracy. This was also replicated in the in vivo model. These results demonstrate that surgical robots exhibiting high levels of autonomy have the potential to improve consistency, patient outcomes, and access to a standard surgical technique.

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

This paper presents a dual-arm suturing robot. We extend the Smart Tissue Autonomous Robot (STAR) with a second robot manipulator, whose purpose is to manage loose suture thread, a task that was previously executed by a human assistant. We also introduce novel near-infrared fluorescent (NIRF) sutures that are automatically segmented and delimit the boundaries of the suturing task. During ex-vivo experiments of porcine models, our results demonstrate that this new system is capable of outperforming human surgeons in all but one metric for the task of vaginal cuff closure (porcine model) and is more consistent in every aspect of the task. We also present results to demonstrate that the system can perform a vaginal cuff closure during an in-vivo experiment (porcine model).

Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called "big data" to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.

Autonomous robotic suturing has the potential to improve surgery outcomes by leveraging accuracy, repeatability, and consistency compared to manual operations. However, achieving full autonomy in complex surgical environments is not practical and human supervision is required to guarantee safety. In this paper, we develop a confidence-based supervised autonomous suturing method to perform robotic suturing tasks via both Smart Tissue Autonomous Robot (STAR) and surgeon collaboratively with the highest possible degree of autonomy. Via the proposed method, STAR performs autonomous suturing when highly confident and otherwise asks the operator for possible assistance in suture positioning adjustments. We evaluate the accuracy of our proposed control method via robotic suturing tests on synthetic vaginal cuff tissues and compare them to the results of vaginal cuff closures performed by an experienced surgeon. Our test results indicate that by using the proposed confidence-based method, STAR can predict the success of pure autonomous suture placement with an accuracy of 94.74%. Moreover, via an additional 25% human intervention, STAR can achieve a 98.1% suture placement accuracy compared to an 85.4% accuracy of completely autonomous robotic suturing. Finally, our experiment results indicate that STAR using the proposed method achieves 1.6 times better consistency in suture spacing and 1.8 times better consistency in suture bite sizes than the manual results.

As a result of major advances in deep learning algorithms and computer processing power, there have been important developments in the fields of medicine and robotics. Although fully autonomous surgery systems where human impact will be minimized are still a long way off, systems with partial autonomy have gradually entered clinical use. In this review, articles on autonomous surgery classified and summarized, with the aim of informing the reader about questions such as "What is autonomic surgery?" and in which areas studies are progressing.

As many meta-analyses comparing pediatric minimally invasive to open surgery can be found in the literature, the aim of this review is to summarize the current state of minimally invasive pediatric surgery and specifically focus on the trends and developments which we expect in the upcoming years. Print and electronic databases were systematically searched for specific keywords, and cross-link searches with references found in the literature were added. Full-text articles were obtained, and eligibility criteria were applied independently. Pediatric minimally invasive surgery is a wide field, ranging from minimally invasive fetal surgery over microlaparoscopy in newborns to robotic surgery in adolescents. New techniques and devices, like natural orifice transluminal endoscopic surgery (NOTES), single-incision and endoscopic surgery, as well as the artificial uterus as a backup for surgery in preterm fetuses, all contribute to the development of less invasive procedures for children. In spite of all promising technical developments which will definitely change the way pediatric surgeons will perform minimally invasive procedures in the upcoming years, one must bear in mind that only hard data of prospective randomized controlled and double-blind trials can validate whether these techniques and devices really improve the surgical outcome of our patients.

In single-port access surgeries, robot size is crucial due to the limited workspace. Thus, a robot may be designed under-actuated. Suturing, in contrast, is a complicated task and requires full actuation. This study aims to overcome this shortcoming by implementing an optimization-based algorithm for autonomous suturing for an under-actuated robot. The proposed algorithm approximates the ideal suturing trajectory by slightly reorienting the needle while deviating as little as possible from the ideal, full degree-of-freedom suturing case. The deviation of the path taken by a custom robot with respect to the ideal trajectory varies depending on the suturing starting location within the workspace as well as the needle size. A quantitative analysis reveals that in 13% of the investigated workspace, the accumulative deviation was less than 10 mm. In the remaining workspace, the accumulative deviation was less than 30 mm. Likewise, the accumulative deviation of a needle with a radius of 10 mm was 2.2 mm as opposed to 8 mm when the radius was 20 mm. The optimization-based algorithm maximized the accuracy of a four-DOF robot to perform a path-constrained trajectory and illustrates the accuracy-workspace correlation.

Haptic based surgical simulations are popular training aids in medicine. Previously, surgical tool loads and motion were measured during cutting and needle insertion on non-human tissue and several haptic based simulations were developed to enhance surgical training. However, there was a lack of realistic foundational data regarding the mechanical responses of human tissue and tools during fundamental acts of surgery, i.e., cutting, suturing, retracting, pinching and indenting. This study used four recently developed surgical tools in a variety of procedures on a diverse set of cadaver leg specimens from human donors. The kinematics and kinetics of surgical tools were recorded along with topical three-dimensional strain during commonly performed surgical procedures. Full motion and load signatures of foundational surgical acts can also be used beyond the development of authentic visual and haptic simulations of surgery, i.e., they provide mechanical specifications for the development of autonomous surgical systems.

Robot-assisted spine surgery has recently emerged as a viable tool to enable less invasive and higher precision surgery. The first-ever spine robot, the SpineAssist (Mazor Robotics Ltd., Caesarea, Israel), gained FDA approval in 2004. With its ability to provide real-time intraoperative navigation and rigid stereotaxy, robotic-assisted surgery has the potential to increase accuracy while decreasing radiation exposure, complication rates, operative time, and recovery time. Currently, robotic assistance is mainly restricted to spinal fusion and instrumentation procedures, but recent studies have demonstrated its use in increasingly complex procedures such as spinal tumor resections and ablations, vertebroplasties, and deformity correction. However, robots do require high initial costs and training, and thus, require justification for their incorporation into common practice. In this review, we discuss the history of spinal robots along as well as currently available systems. We then examine the literature to evaluate accuracy, operative time, complications, radiation exposure, and costs - comparing robotic-assisted to traditional fluoroscopy-assisted freehand approaches. Finally, we consider future applications for robots in spine surgery.

Autonomous robotic anastomosis has the potential to improve surgical outcomes by performing more consistent suture spacing and bite size compared to manual anastomosis. However, due to soft tissue's irregular shape and unpredictable deformation, performing autonomous robotic anastomosis without continuous tissue detection and three-dimensional path planning strategies remains a challenging task. In this paper, we present a novel three-dimensional path planning algorithm for Smart Tissue Autonomous Robot (STAR) to enable semi-autonomous robotic anastomosis on deformable tissue. The algorithm incorporates (i) continuous detection of 3D near infrared (NIR) markers manually placed on deformable tissue before the procedure, (ii) generating a uniform and consistent suture placement plan using 3D path planning methods based on the locations of the NIR markers, and (iii) updating the remaining suture plan after each completed stitch using a non-rigid registration technique to account for tissue deformation during anastomosis. We evaluate the path planning algorithm for accuracy and consistency by comparing the anastomosis of synthetic vaginal cuff tissue completed by STAR and a surgeon. Our test results indicate that STAR using the proposed method achieves 2.6 times better consistency in suture spacing and 2.4 times better consistency in suture bite sizes than the manual anastomosis.

Oral squamous cell carcinoma (OSCC) is the most common cancer in the head and neck region, and is associated with high morbidity and mortality rates. Surgical resection is usually the primary treatment strategy for OSCC, and maintaining effective tumor resection margins is paramount to surgical outcomes. In practice, wide tumor excisions impair post-surgical organ function, while narrow resection margins are associated with tumor recurrence. Identification and tracking of these resection margins remain a challenge because they migrate and shrink from preoperative chemo or radiation therapies, and deform intra-operatively. This paper reports a novel near-infrared (NIR) fluorescent marking and landmark-based deformable image registration (DIR) method to precisely predict deformed margins. The accuracy of DIR predicted resection margins on porcine cadaver tongues is compared with rigid image registration and surgeon's manual prediction. Furthermore, our tracking and registration technique is integrated into a robotic system, and tested using ex vivo porcine cadaver tongues to demonstrate the feasibility of supervised autonomous tumor bed resections.

This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a particular focus on ethics, regulation and legal aspects (such as civil law, international law, tort law, liability, medical malpractice, privacy and product/device legislation, among other aspects).

We conducted an intensive literature search on current or emerging AI and autonomous technologies (eg, vehicles), military and medical technologies (eg, surgical robots), relevant frameworks and standards, cyber security/safety- and legal-systems worldwide. We provide a discussion on unique challenges for robotic surgery faced by proposals made for AI more generally (eg, Explainable AI) and machine learning more specifically (eg, black box), as well as recommendations for developing and improving relevant frameworks or standards.

We classify responsibility into the following: (1) Accountability; (2) Liability; and (3) Culpability. All three aspects were addressed when discussing responsibility for AI and autonomous surgical robots, be these civil or military patients (however, these aspects may require revision in cases where robots become citizens). The component which produces the least clarity is Culpability, since it is unthinkable in the current state of technology. We envision that in the near future a surgical robot can learn and perform routine operative tasks that can then be supervised by a human surgeon. This represents a surgical parallel to autonomously driven vehicles. Here a human remains in the 'driving seat' as a 'doctor-in-the-loop' thereby safeguarding patients undergoing operations that are supported by surgical machines with autonomous capabilities.

Compared to open surgical techniques, laparoscopic surgical methods aim to reduce the collateral tissue damage and hence decrease the patient recovery time. However, constraints imposed by the laparoscopic surgery, i.e. the operation of surgical tools in limited spaces, turn simple surgical tasks such as suturing into time-consuming and inconsistent tasks for surgeons. In this paper, we develop an autonomous laparoscopic robotic suturing system. More specific, we expand our smart tissue anastomosis robot (STAR) by developing i) a new 3D imaging endoscope, ii) a novel actuated laparoscopic suturing tool, and iii) a suture planning strategy for the autonomous suturing. We experimentally test the accuracy and consistency of our developed system and compare it to sutures performed manually by surgeons. Our test results on suture pads indicate that STAR can reach 2.9 times better consistency in suture spacing compared to manual method and also eliminate suture repositioning and adjustments. Moreover, the consistency of suture bite sizes obtained by STAR matches with those obtained by manual suturing.

Europe has changed remarkably over the past decades and so have concepts and outcomes of esophageal atresia repair. In this article, both the efforts to create a united Europe and the achievements in dealing with esophageal atresia from the 1950s on are outlined. Furthermore, this paper deals with the future of pediatric surgery and is focused on two aspects: the "Fourth Industrial Revolution" which builds on the digital revolution, artificial intelligence and robotics, and its potential impact on pediatric surgery and the life of patients. I suggest that pediatric surgeons should participate and lead in the development of machine learning, data control, assuring appropriate use of machines, control misuse, and in particular ensure appropriate maintenance of ethical standards. Changes in health care structures within Europe, in particular the effect of centralization, will affect the concept of treatment for patients with rare diseases.

In order to realize many of the potential benefits associated with robotically assisted minimally invasive surgery, the robot must be more than a remote controlled device. Currently, using a surgical robot can be challenging, fatiguing, and time consuming. Teaching the robot to actively assist surgical tasks, such as suturing, has the potential to vastly improve both patient outlook and the surgeon's efficiency. One obstacle to completing surgical sutures autonomously is the difficulty in tracking surgical suture threads. This paper presents novel stereo image processing algorithms for the detection, initialization, and tracking of a surgical suture thread. A Non Uniform Rational B-Spline (NURBS) curve is used to model a thin, deformable, and dynamic length thread. The NURBS model is initialized and grown from a single selected point located on the thread. The NURBS curve is optimized by minimizing the image matching energy between the projected stereo NURBS image and the segmented thread image. The algorithms are evaluated using suture threads, a calibrated test pattern, and a simulated thread image. Additionally, the accuracy of the algorithms presented are validated as they track a suture thread undergoing translation, deformation, and apparent length changes. All of the tracking is in real-time. Note to Practioners: Abstract-The problem of tracking a surgical suture thread was addressed in this work. Since the suture thread is highly deformable, any tracking algorithm must be robust to intersections, occlusions, knot tying, and length changes. The detection algorithm introduced in this paper is capable of distinguishing different threads when they intersect. The tracking algorithm presented here demonstrate that it is possible, using polynomial curves, to track a suture thread as it deforms, becomes occluded, changes length, and even ties a knot in real time. The detection algorithm can enhance directional thin features while the polynomial curve modeling can track any string like structure. Further integration of the polynomial curve with a feed-forward thread model could improve the stability and robustness of the thread tracking.

This paper reports the design, development, and initial evaluation of a robotic laparoscopic clipping tool for single manipulator wound closure and anastomosis (tubular reconnection). The tool deploys biodegradable clips and clasps with the goal of (i) integrating grasping and suturing into a single device for single hand or manipulator use, (ii) applying the equivalent of interrupted sutures without the need of managing suture thread, and (iii) allowing for full six degrees-of-freedom (DOFs) laparoscopic control when mounted on a robot arm. The specifications, workflow, and detailed design of the robotic laparoscopic tool and injection molded bio-absorbable T shaped clip and locking clasp are reported. The clipping tool integrates forceps to grab and stabilize tissue and a clip and clasp applier to approximate and fixate the tissue. A curved needle is advanced on a circular needle path and picks up and drags clips through tissue. The clip is then tightened through the tissue and a clasp is clamped around the clip, before the clip is released from the needle. Results of several bench test runs of the tool show: (a) repeatable circular needle drive, (b) successful pick-up and deployment of clips, (c) successful shear of the clip to release the clip from the needle, and (d) closure of clasp on clip with an average of 2.0 N holding force. These data indicate that the robotic laparoscopic clipping tool could be used for laparoscopic wound closure and anastomosis.

This paper specifies a surgical robot performing semi-autonomous electrosurgery for tumor resection and evaluates its accuracy using a visual servoing paradigm. We describe the design and integration of a novel, multi-degree of freedom electrosurgical tool for the smart tissue autonomous robot (STAR). Standardized line tests are executed to determine ideal cut parameters in three different types of porcine tissue. STAR is then programmed with the ideal cut setting for porcine tissue and compared against expert surgeons using open and laparoscopic techniques in a line cutting task. We conclude with a proof of concept demonstration using STAR to semi-autonomously resect pseudo-tumors in porcine tissue using visual servoing. When tasked to excise tumors with a consistent 4mm margin, STAR can semi-autonomously dissect tissue with an average margin of 3.67 mm and a standard deviation of 0.89mm.

The current paradigm of robot-assisted surgeries (RASs) depends entirely on an individual surgeon's manual capability. Autonomous robotic surgery-removing the surgeon's hands-promises enhanced efficacy, safety, and improved access to optimized surgical techniques. Surgeries involving soft tissue have not been performed autonomously because of technological limitations, including lack of vision systems that can distinguish and track the target tissues in dynamic surgical environments and lack of intelligent algorithms that can execute complex surgical tasks. We demonstrate in vivo supervised autonomous soft tissue surgery in an open surgical setting, enabled by a plenoptic three-dimensional and near-infrared fluorescent (NIRF) imaging system and an autonomous suturing algorithm. Inspired by the best human surgical practices, a computer program generates a plan to complete complex surgical tasks on deformable soft tissue, such as suturing and intestinal anastomosis. We compared metrics of anastomosis-including the consistency of suturing informed by the average suture spacing, the pressure at which the anastomosis leaked, the number of mistakes that required removing the needle from the tissue, completion time, and lumen reduction in intestinal anastomoses-between our supervised autonomous system, manual laparoscopic surgery, and clinically used RAS approaches. Despite dynamic scene changes and tissue movement during surgery, we demonstrate that the outcome of supervised autonomous procedures is superior to surgery performed by expert surgeons and RAS techniques in ex vivo porcine tissues and in living pigs. These results demonstrate the potential for autonomous robots to improve the efficacy, consistency, functional outcome, and accessibility of surgical techniques.

With the growing interest in advanced image-guidance for surgical robot systems, rapid integration and testing of robotic devices and medical image computing software are becoming essential in the research and development. Maximizing the use of existing engineering resources built on widely accepted platforms in different fields, such as robot operating system (ROS) in robotics and 3D Slicer in medical image computing could simplify these tasks. We propose a new open network bridge interface integrated in ROS to ensure seamless cross-platform data sharing.

A ROS node named ROS-IGTL-Bridge was implemented. It establishes a TCP/IP network connection between the ROS environment and external medical image computing software using the OpenIGTLink protocol. The node exports ROS messages to the external software over the network and vice versa simultaneously, allowing seamless and transparent data sharing between the ROS-based devices and the medical image computing platforms.

Performance tests demonstrated that the bridge could stream transforms, strings, points, and images at 30 fps in both directions successfully. The data transfer latency was <1.2 ms for transforms, strings and points, and 25.2 ms for color VGA images. A separate test also demonstrated that the bridge could achieve 900 fps for transforms. Additionally, the bridge was demonstrated in two representative systems: a mock image-guided surgical robot setup consisting of 3D slicer, and Lego Mindstorms with ROS as a prototyping and educational platform for IGT research; and the smart tissue autonomous robot surgical setup with 3D Slicer.

The study demonstrated that the bridge enabled cross-platform data sharing between ROS and medical image computing software. This will allow rapid and seamless integration of advanced image-based planning/navigation offered by the medical image computing software such as 3D Slicer into ROS-based surgical robot systems.

Robotically Assisted Minimally Invasive Surgery (RAMIS) offers many advantages over manual surgical techniques. Most of the limitations of RAMIS stem from its non-intuitive user interface and costs. One way to mitigate some of the limitations is to automate surgical subtasks (e.g. suturing) such that they are performed faster while allowing the surgeon to plan the next step of the procedure. One component of successful suture automation is minimizing the internal tissue deformation forces generated by driving a needle through tissue. Minimizing the internal tissue forces requires segmenting the tissue deformation forces from other components of the needle tissue interaction (e.g. friction force). This paper proposes an Unscented Kalman Filter which can successfully model the force components, in particular the internal deformation force, generated by a needle as it is driven through a sample of tissue.

Three-dimensional sensing of changing surgical scenes would improve the function of surgical robots. This paper explores the requirements and utility of a new type of depth sensor, the plenoptic camera, for surgical robots. We present a metric calibration procedure for the plenoptic camera and the registration of its coordinate frame to the robot (hand-eye calibration). We also demonstrate the utility in robotic needle insertion and application of sutures in phantoms. The metric calibration accuracy is reported as 1.14 ± 0.80 mm for the plenoptic camera and 1.57 ± 0.90 mm for hand-eye calibration. The accuracy of needle insertion task is 1.79 ± 0.35 mm for the entire robotic system. Additionally, the accuracy of suture placement with the presented system is reported at 1.80 ± 0.43 mm. Finally, we report consistent suture spacing with only 0.11 mm standard deviation between inter-suture distances. The measured accuracy of less than 2 mm with consistent suture spacing is a promising result to provide repeatable leak-free suturing with a robotic tool and a plenoptic depth imager.

This paper presents algorithms for selection of needle grasp and for selection of entry ports of robotic instruments, for autonomous robotic execution of the minimally invasive surgical suturing task. A critical issue for automatic execution of surgical tasks, such as suturing, is the choice of needle grasp for the robotic system. Inappropriate needle grasp increases operating time requiring multiple regrasps to complete the desired task. In robotic minimally invasive surgery, the entry port that the surgical robot goes through into the patient's body has a significant role on the performance of the robot. Improper entry port affects the robot's dexterity, manipulability and reachability. The proposed methods use manipulability, dexterity and torque metrics for needle grasp selection, and employ needle grasp robustness and target location robustness metrics for port selection. The results of a case study simulation in thoracoscopic surgery is also presented to demonstrate the proposed methods. Note to Practitioners-This paper is motivated by the problem of automating low-level surgical tasks in robotic surgery, such as, suturing, retraction, dissection, and providing exposure. Specifically, this paper focuses on needle grasp and entry port selection for automating robotic surgical suturing. Selection of an appropriate way of grasping a needle is critical for successfully and robustly completing autonomous suturing. To the best authors' knowledge, there are no earlier studies in the literature which focus on the needle grasp selection problem. The proposed approach determines how to grasp the needle by optimizing the surgical system's manipulation performance. The existing approaches in the literature for selecting entry ports for the robotic surgical tools only consider the teleoperated robotic minimally invasive surgery, in which the surgeons directly control the robotic instruments. However, automated performance of suturing introduces additional challenges due to uncertainties in needle localization and grasping. This paper proposes two new performance metrics on selecting port locations from the perspective of autonomously performing surgical suturing, without direct involvement of the human user. The paper also presents preliminary experiments which demonstrate the effectiveness of the proposed methods.

Suturing is one of the more tiresome and difficult tasks during laparoscopic surgeries. To cope with this problem, we aimed to develop a novel successive suturing device. A novel needle holding and locking mechanism is proposed to transfer the needle between the upper and bottom jaws. The device is straightforward to use with intuitive 2-trigger control, and it can perform successive suturing without the need of reload between stiches. Also, it is compact enough to be inserted through a 12-mm trocar. The feasibility of the device is verified through in vitro and in vivo experiments. It was found that the developed device was able to successfully close the wounds without any leakage. The developed successive suturing device offers an easy way of performing suture, and it will greatly help surgeons during laparoscopic surgeries.

The role of robot-assisted surgery in children remains controversial. This article aims to distil this debate into an evidence informed decision-making taxonomy; to adopt this technology (1) now, (2) later, or (3) not at all. Robot-assistance is safe, feasible and effective in selected cases as an adjunctive tool to enhance capabilities of minimally invasive surgery, as it is known today. At present, expectations of rigid multi-arm robotic systems to deliver higher quality care are over-estimated and poorly substantiated by evidence. Such systems are associated with high costs. Further comparative effectiveness evidence is needed to define the case-mix for which robot-assistance might be indicated. It seems unlikely that we should expect compelling patient benefits when it is only the mode of minimally invasive surgery that differs. Only large higher-volume institutions that share the robot amongst multiple specialty groups are likely to be able to sustain higher associated costs with today's technology. Nevertheless, there is great potential for next-generation surgical robotics to enable better ways to treat childhood surgical diseases through less invasive techniques that are not possible today. This will demand customized technology for selected patient populations or procedures. Several prototype robots exclusively designed for pediatric use are already under development. Financial affordability must be a high priority to ensure clinical accessibility.

Intestinal anastomosis is a surgical procedure that restores bowel continuity after surgical resection to treat intestinal malignancy, inflammation, or obstruction. Despite the routine nature of intestinal anastomosis procedures, the rate of complications is high. Standard visual inspection cannot distinguish the tissue subsurface and small changes in spectral characteristics of the tissue, so existing tissue anastomosis techniques that rely on human vision to guide suturing could lead to problems such as bleeding and leakage from suturing sites. We present a proof-of-concept study using a portable multispectral imaging (MSI) platform for tissue characterization and preoperative surgical planning in intestinal anastomosis. The platform is composed of a fiber ring light-guided MSI system coupled with polarizers and image analysis software. The system is tested on ex vivo porcine intestine tissue, and we demonstrate the feasibility of identifying optimal regions for suture placement.