Autonomous robotic laparoscopic surgery for intestinal anastomosis

Epistemic (in)justice, social identity and the Black Box problem in patient care

This manuscript draws on the moral norms arising from the nuanced accounts of epistemic (in)justice and social identity in relational autonomy to normatively assess and articulate the ethical problems associated with using AI in patient care in light of the Black Box problem. The article also describes how black-boxed AI may be used within the healthcare system. The manuscript highlights what needs to happen to align AI with the moral norms it draws on. Deeper thinking - from other backgrounds other than decolonial scholarship and relational autonomy - about the impact of AI on the human experience needs to be done to appreciate any other barriers that may exist. Future studies can take up this task.

Recent Advances in Digital Technology in Implant Dentistry

Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.

Applications of artificial intelligence in urologic oncology

PURPOSE

With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology.

MATERIALS AND METHODS

We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery."

RESULTS

A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.

CONCLUSIONS

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Artificial intelligence in surgery

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues' attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

SurgNet: Self-Supervised Pretraining With Semantic Consistency for Vessel and Instrument Segmentation in Surgical Images

Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (RAG) based on local semantic consistency in unlabeled surgical images and use it as a self-supervision signal for pseudo-mask segmentation. We then use the pseudo-mask to perform guided masked image modeling (GMIM) to learn representations that integrate structural information of intraoperative objectives more effectively. Our pretrained model, paired with various segmentation methods, can be applied to perform vessel and instrument segmentation accurately using limited labeled data for fine-tuning. We build an Intraoperative Vessel and Instrument Segmentation (IVIS) dataset, comprised of ~3 million unlabeled images and over 4,000 labeled images with manual vessel and instrument annotations to evaluate the effectiveness of our self-supervised pretraining method. We also evaluated the generalizability of our method to similar tasks using two public datasets. The results demonstrate that our approach outperforms the current state-of-the-art (SOTA) self-supervised representation learning methods in various surgical image segmentation tasks.

Robotic colorectal surgery and future directions

As the adoption of robotic-assisted procedures expands across various surgical specialties, colorectal surgery stands out as a prominent beneficiary. This rise in usage can be traced back to the increased accessibility of robotic platforms and a growing institutional shift towards cutting-edge surgical methods. When compared with traditional laparoscopic methods, robotic techniques offer distinct advantages. Their true potential shines in surgeries involving complex anatomical regions, where the robot's enhanced dexterity and range of motion prove invaluable. The three-dimensional, magnified view provided by robotic systems further boosts surgical precision and clarity. These advantages render robotic assistance especially suitable for colorectal surgeries, notably in intricate areas such as the rectum and endoluminal spaces. As the medical world emphasizes minimally invasive surgical methods, there's a pressing need to evolve and optimize robotic techniques in colorectal surgery. This article traces the evolution of robotic interventions in colorectal surgeries, highlighting both its historical milestones and anticipated future trends. We'll also explore emerging robotic tools and systems set to reshape the colorectal surgical arena.

Comparison Results of Three-Port Robot-Assisted and Uniportal Video-Assisted Lobectomy for Functional Recovery Index in the Treatment of Early Stage Non-small Cell Lung Cancer: A Propensity Score-Matched Analysis

BACKGROUND

Minimally invasive lobectomy is the standard treatment for early stage non-small cell lung cancer (NSCLC). The aim of this study is to investigate postoperative recovery in a prospective trial of discharged patients with early stage non-small cell lung cancer undergoing robot-assisted thoracic surgery (RATS) versus uniportal video-assisted thoracic surgery (UVATS).

PATIENTS AND METHODS

This is a prospective and observational study. From 9 September 2022 to 1 July 2023, 178 patients diagnosed with NSCLC admitted to the Department of Thoracic Surgery of Shandong Provincial Hospital signed informed consent and underwent lobectomy by RATS and UVATS. The functional recovery index included MD Anderson Symptom Inventory, Christensen Fatigue Scale, EORTC QLQ-C30, and Leicester Cough Questionnaire.

RESULTS

After propensity score-matched analysis, each group included 42 cases. For the baseline characteristics of patients, operation time (p = 0.01) and length of stay (p = 0.04) were shorter in the RATS group. The number of lymph nodes resected in the RATS group was much more than in the UVATS group. According to our investigation, appetite loss, nausea, diarrhea, and cough severity after RATS were better than after UVATS. After the first week, pain severity degree of the RATS group was higher than UVATS, while there was no difference during the second and third week. The physical score of the RATS group was higher than the UVATS group (p = 0.04), according to the Leicester Cough Questionnaire.

CONCLUSION

RATS was associated with severe short-term postoperative pain but less postoperative complications.

Clinical applications of artificial intelligence in robotic surgery

Artificial intelligence (AI) is revolutionizing nearly every aspect of modern life. In the medical field, robotic surgery is the sector with some of the most innovative and impactful advancements. In this narrative review, we outline recent contributions of AI to the field of robotic surgery with a particular focus on intraoperative enhancement. AI modeling is allowing surgeons to have advanced intraoperative metrics such as force and tactile measurements, enhanced detection of positive surgical margins, and even allowing for the complete automation of certain steps in surgical procedures. AI is also Query revolutionizing the field of surgical education. AI modeling applied to intraoperative surgical video feeds and instrument kinematics data is allowing for the generation of automated skills assessments. AI also shows promise for the generation and delivery of highly specialized intraoperative surgical feedback for training surgeons. Although the adoption and integration of AI show promise in robotic surgery, it raises important, complex ethical questions. Frameworks for thinking through ethical dilemmas raised by AI are outlined in this review. AI enhancements in robotic surgery is some of the most groundbreaking research happening today, and the studies outlined in this review represent some of the most exciting innovations in recent years.

A Novel Vascular Anastomotic Coupling Device for End-to-End Anastomosis of Arteries and Veins

OBJECTIVE

Hand-sutured (HS) techniques remain the gold standard for most microvascular anastomoses in microsurgery. HS techniques can result in endothelial lacerations and back wall suturing, leading to complications such as thrombosis and free tissue loss. A novel force-interference-fit vascular coupling device (FIF-VCD) system can potentially reduce the need for HS and improve end-to-end anastomosis. This study aims to describe the development and testing of a novel FIF-VCD system for 1.5 to 4.0 mm outside diameter arteries and veins.

METHODS

Benchtop anastomoses were performed using porcine cadaver arteries and veins. Decoupling force and anastomotic leakage were tested under simulated worst-case intravital physiological conditions. The 1.5 mm FIF-VCD system was used to perform cadaver rat abdominal aorta anastomoses.

RESULTS

Benchtop testing showed that the vessels coupled with the FIF-VCD system could withstand simulated worst-case intravital physiological conditions with a 95% confidence interval for the average decoupling force safety factor of 8.2 ± 1.0 (5.2 ± 1.0 N) and a 95% confidence interval for the average leakage rate safety factor of 26 ± 3.6 (8.4 ± 0.14 and 95 ± 1.4 μL/s at 150 and 360 mmHg, respectively) when compared to HS anastomotic leakage rates (310 ± 14 and 2,100 ± 72 μL/s at 150 and 360 mmHg, respectively). The FIF-VCD system was successful in performing cadaver rat abdominal aorta anastomoses.

CONCLUSION

The FIF-VCD system can potentially replace HS in microsurgery, allowing the safe and effective connection of arteries and veins. Further studies are needed to confirm the clinical viability and effectiveness of the FIF-VCD system.

Autonomous System for Tumor Resection (ASTR) - Dual-Arm Robotic Midline Partial Glossectomy

Head and neck cancers are the seventh most common cancers worldwide, with squamous cell carcinoma being the most prevalent histologic subtype. Surgical resection is a primary treatment modality for many patients with head and neck squamous cell carcinoma, and accurately identifying tumor boundaries and ensuring sufficient resection margins are critical for optimizing oncologic outcomes. This study presents an innovative autonomous system for tumor resection (ASTR) and conducts a feasibility study by performing supervised autonomous midline partial glossectomy for pseudotumor with millimeter accuracy. The proposed ASTR system consists of a dual-camera vision system, an electrosurgical instrument, a newly developed vacuum grasping instrument, two 6-DOF manipulators, and a novel autonomous control system. The letter introduces an ontology-based research framework for creating and implementing a complex autonomous surgical workflow, using the glossectomy as a case study. Porcine tongue tissues are used in this study, and marked using color inks and near-infrared fluorescent (NIRF) markers to indicate the pseudotumor. ASTR actively monitors the NIRF markers and gathers spatial and color data from the samples, enabling planning and execution of robot trajectories in accordance with the proposed glossectomy workflow. The system successfully performs six consecutive supervised autonomous pseudotumor resections on porcine specimens. The average surface and depth resection errors measure 0.73±0.60 mm and 1.89±0.54 mm, respectively, with no positive tumor margins detected in any of the six resections. The resection accuracy is demonstrated to be on par with manual pseudotumor glossectomy performed by an experienced otolaryngologist.

Innovation and disruptive science determine the future of cardiothoracic surgery

One of the currently most asked questions in the field of medicine is how any specialty in the future will evolve to ensure better health for the patients by using current, unparalleled developments in all areas of science. This article will give an overview of new and evolving strategies for cardiothoracic (CT) surgery that are available today and will become available in the future in order to achieve this goal. In the founding era of CT surgery in the 1950s and 1960s, there was tremendous excitement about innovation and disruptive science, which eventually resulted in a completely new medical specialty, i.e. CT surgery. Entirely new treatment strategies were introduced for many cardiovascular diseases that had been considered incurable until then. As expected, alternative techniques have evolved in all fields of science during the last few decades, allowing great improvements in diagnostics and treatment in all medical specialties. The future of CT surgery will be determined by an unrestricted and unconditional investment in innovation, disruptive science and our own transformation using current achievements from many other fields. From the multitude of current and future possibilities, I will highlight 4 in this review: improvements in our current techniques, bringing CT surgery to low- and middle-income countries, revolutionizing the perioperative period and treating as yet untreatable diseases. These developments will allow us a continuation of the previously unheard-of treatment possibilities provided by ingenious innovations based on the fundamentals of CT surgery.

Defining digital surgery: a SAGES white paper

BACKGROUND

Digital surgery is a new paradigm within the surgical innovation space that is rapidly advancing and encompasses multiple areas.

METHODS

This white paper from the SAGES Digital Surgery Working Group outlines the scope of digital surgery, defines key terms, and analyzes the challenges and opportunities surrounding this disruptive technology.

RESULTS

In its simplest form, digital surgery inserts a computer interface between surgeon and patient. We divide the digital surgery space into the following elements: advanced visualization, enhanced instrumentation, data capture, data analytics with artificial intelligence/machine learning, connectivity via telepresence, and robotic surgical platforms. We will define each area, describe specific terminology, review current advances as well as discuss limitations and opportunities for future growth.

CONCLUSION

Digital Surgery will continue to evolve and has great potential to bring value to all levels of the healthcare system. The surgical community has an essential role in understanding, developing, and guiding this emerging field.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

AI co-pilot bronchoscope robot

The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI-human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance.

The LMIT: Light-mediated minimally-invasive theranostics in oncology

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

Autonomous Robotic Surgery: Has the Future Arrived?

Autonomous robotic surgery represents a pioneering field dedicated to the integration of robotic systems with varying degrees of autonomy for the execution of surgical procedures. This paradigm shift is made possible by the progressive integration of artificial intelligence (AI) and machine learning (ML) into the realm of surgical interventions. While the majority of autonomous robotic systems remain in the experimental phase, a notable subset has successfully transitioned into clinical applications. Noteworthy procedures, such as venipuncture, hair implantations, intestinal anastomosis, total knee replacement, cochlear implant, radiosurgery, and knot tying, among others, exemplify the current capabilities of autonomous surgical systems. This review endeavors to comprehensively address facets of autonomous robotic surgery, commencing with a concise elucidation of fundamental concepts and traversing the pivotal milestones in the historical evolution of robotic surgery. This historical trajectory underscores the incremental assimilation of autonomous systems into surgical practices. This review aims to address topics related to autonomous robotic surgery, starting with a description of fundamental concepts and going through the milestones in robotic surgery history that also show the gradual incorporations of autonomous systems. It also includes a discussion of the key benefits and risks of this technology, the degrees of autonomy in surgical robots, their limitations, the current legal regulations governing their usage, and the main ethical concerns inherent to their nature.

Intraoperative Applications of Artificial Intelligence in Robotic Surgery: A Scoping Review of Current Development Stages and Levels of Autonomy

OBJECTIVE

A scoping review of the literature was conducted to identify intraoperative artificial intelligence (AI) applications for robotic surgery under development and categorize them by (1) purpose of the applications, (2) level of autonomy, (3) stage of development, and (4) type of measured outcome.

BACKGROUND

In robotic surgery, AI-based applications have the potential to disrupt a field so far based on a master-slave paradigm. However, there is no available overview about this technology's current stage of development and level of autonomy.

METHODS

MEDLINE and EMBASE were searched between January 1, 2010 and May 21, 2022. Abstract screening, full-text review, and data extraction were performed independently by 2 reviewers. The level of autonomy was defined according to the Yang and colleagues' classification and stage of development according to the Idea, Development, Evaluation, Assessment, and Long-term follow-up framework.

RESULTS

One hundred twenty-nine studies were included in the review. Ninety-seven studies (75%) described applications providing Robot Assistance (autonomy level 1), 30 studies (23%) application enabling Task Autonomy (autonomy level 2), and 2 studies (2%) application achieving Conditional autonomy (autonomy level 3). All studies were at Idea, Development, Evaluation, Assessment, and Long-term follow-up stage 0 and no clinical investigations on humans were found. One hundred sixteen (90%) conducted in silico or ex vivo experiments on inorganic material, 9 (7%) ex vivo experiments on organic material, and 4 (3%) performed in vivo experiments in porcine models.

CONCLUSIONS

Clinical evaluation of intraoperative AI applications for robotic surgery is still in its infancy and most applications have a low level of autonomy. With increasing levels of autonomy, the evaluation focus seems to shift from AI-specific metrics to process outcomes, although common standards are needed to allow comparison between systems.

Soft tissue surgical robot for minimally invasive surgery: a review

Purpose

The current state of soft tissue surgery robots is surveyed, and the key technologies underlying their success are analyzed. State-of-the-art technologies are introduced, and future directions are discussed.

Methods

Relevant literature is explored, analyzed, and summarized.

Results

Soft tissue surgical robots had rapidly spread in the field of laparoscopic surgery based on the multi-degree-of-freedom movement of intra-abdominal surgical tools and stereoscopic imaging that are not possible in conventional surgery. The three key technologies that have made surgical robots successful are wire-driven mechanisms for multi-degree-of-freedom movement, master devices for intuitive remote control, and stereoscopic imaging technology. Recently, human-robot interaction technologies have been applied to develop user interfaces such as vision assistance and haptic feedback, and research on autonomous surgery has begun.

Conclusion

Robotic surgery not only replaces conventional laparoscopic surgery but also allows for complex surgeries that are not possible with laparoscopic surgery. On the other hand, it is also criticized for its high cost and lack of clinical superiority or patient benefit compared to conventional laparoscopic surgery. As various robots compete in the market, the cost of surgical robots is expected to decrease. Surgical robots are expected to continue to evolve in the future due to the need to reduce the workload of medical staff and improve the level of care demanded by patients.

Neural signals-based respiratory motion tracking: a proof-of-concept study

Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking.Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87.Significance.Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.

Autonomous medical needle steering in vivo

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safely and accurately navigating a needle through living tissue to a target is currently often challenging or infeasible because of the presence of anatomical obstacles, high levels of uncertainty, and natural tissue motion. Medical robots capable of automating needle-based procedures have the potential to overcome these challenges and enable enhanced patient care and safety. However, autonomous navigation of a needle around obstacles to a predefined target in vivo has not been shown. Here, we introduce a medical robot that autonomously navigates a needle through living tissue around anatomical obstacles to a target in vivo. Our system leverages a laser-patterned highly flexible steerable needle capable of maneuvering along curvilinear trajectories. The autonomous robot accounts for anatomical obstacles, uncertainty in tissue/needle interaction, and respiratory motion using replanning, control, and safe insertion time windows. We applied the system to lung biopsy, which is critical for diagnosing lung cancer, the leading cause of cancer-related deaths in the United States. We demonstrated successful performance of our system in multiple in vivo porcine studies achieving targeting errors less than the radius of clinically relevant lung nodules. We also demonstrated that our approach offers greater accuracy compared with a standard manual bronchoscopy technique. Our results show the feasibility and advantage of deploying autonomous steerable needle robots in living tissue and how these systems can extend the current capabilities of physicians to further improve patient care.

Physical and mental health impairments experienced by operating surgeons and camera-holder assistants during laparoscopic surgery: a cross-sectional survey

Introduction

Surgeons may experience physical and mental health problems because of their jobs, which may lead to chronic muscle damage, burnout, or even withdrawal. However, these are often ignored in camera-holder assistants during laparoscopic surgery. We aimed to analyze the differences between operating surgeons and camera-holder assistants.

Methods

From January 1, 2022, to December 31, 2022, a cross-sectional survey was conducted to evaluate the muscle pain, fatigue, verbal scolding, and task load for operating surgeons and camera-holder assistants. The Nordic Musculoskeletal Questionnaire, the Space Administration Task Load Index, and the Surgical Task Load Index (SURG-TLX) were combined in the questionnaire.

Results

2,184 operations were performed by a total of 94 operating surgeons and 220 camera assistants. 81% of operating surgeons and 78% of camera-holder assistants reported muscle pain/discomfort during the procedure. The most affected anatomic region was the shoulders for operating surgeons, and the lower back for camera-holder assistants. Intraoperative fatigue was reported by 41.7% of operating surgeons and 51.7% of camera-holder assistants. 55.2% of camera-holder assistants reported verbal scolding from the operating surgeons, primarily attributed to lapses in laparoscope movement coordination. The SURG-TLX results showed that the distributions of mental, physical, and situational stress for operating surgeons and camera-holder assistants were comparable.

Conclusion

Like operating surgeons, camera-holder assistants also face similar physical and mental health impairments while performing laparoscopic surgery. Improvements to the working conditions of the camera-holder assistant should not be overlooked.

Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein

BACKGROUND

Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of adrenal veins are critical parts of adrenal surgery. The utilization of artificial intelligence and deep learning algorithms to identify anatomic structures during laparoscopic and robot-assisted surgery can be used to provide real-time guidance.

METHODS

In this experimental feasibility study, intraoperative videos of patients who underwent minimally invasive transabdominal left adrenalectomy procedures between 2011 and 2022 in a tertiary endocrine referral center were retrospectively analyzed and used to develop an artificial intelligence model. Semantic segmentation of the left adrenal vein with deep learning was performed. To train a model, 50 random images per patient were captured during the identification and dissection of the left adrenal vein. A randomly selected 70% of data was used to train models while 15% for testing and 15% for validation with 3 efficient stage-wise feature pyramid networks (ESFPNet). Dice similarity coefficient (DSC) and intersection over union scores were used to evaluate segmentation accuracy.

RESULTS

A total of 40 videos were analyzed. Annotation of the left adrenal vein was performed in 2000 images. The segmentation network training on 1400 images was used to identify the left adrenal vein in 300 test images. The mean DSC and sensitivity for the highest scoring efficient stage-wise feature pyramid network B-2 network were 0.77 (±0.16 SD) and 0.82 (±0.15 SD), respectively, while the maximum DSC was 0.93, suggesting a successful prediction of anatomy.

CONCLUSIONS

Deep learning algorithms can predict the left adrenal vein anatomy with high performance and can potentially be utilized to identify critical anatomy during adrenal surgery and provide real-time guidance in the near future.

Artificial intelligence meets medical robotics

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.

Robotic Surgery: A Comprehensive Review of the Literature and Current Trends

Robotic surgery (RS) is an evolution of minimally invasive surgery that combines medical science, robotics, and engineering. The first robots approved by the Food and Drug Administration (FDA) were the Da Vinci Surgical System and the ZEUS Robotic Surgical System, which have been improving over time. Through the decades, the equipment applied to RS had undergone a wide transformation as a response to the development of new techniques and facilities for the assembly and implementation of the own. RS has revolutionized the field of urology, enabling surgeons to perform complex procedures with greater precision and accuracy, and many other surgical specialties such as gynecology, general surgery, otolaryngology, cardiothoracic surgery, and neurosurgery. Several benefits, such as a better approach to the surgical site, a three-dimensional image that improves depth perception, and smaller scars, enhance range of motion, allowing the surgeon to conduct more complicated surgical operations, and reduced postoperative complications have made robotic-assisted surgery an increasingly popular approach. However, some points like the cost of surgical procedures, equipment-instrument, and maintenance are important aspects to consider. Machine learning will likely have a role to play in surgical training shortly through "automated performance metrics," where algorithms observe and "learn" individual surgeons' techniques, assess performance, and anticipate surgical outcomes with the potential to individualize surgical training and aid decision-making in real time.

Evolution of a surgical system using deep learning in minimally invasive surgery (Review)

Recently, artificial intelligence (AI) has been applied in various fields due to the development of new learning methods, such as deep learning, and the marked progress in computational processing speed. AI is also being applied in the medical field for medical image recognition and omics analysis of genomes and other data. Recently, AI applications for videos of minimally invasive surgeries have also advanced, and studies on such applications are increasing. In the present review, studies that focused on the following topics were selected: i) Organ and anatomy identification, ii) instrument identification, iii) procedure and surgical phase recognition, iv) surgery-time prediction, v) identification of an appropriate incision line, and vi) surgical education. The development of autonomous surgical robots is also progressing, with the Smart Tissue Autonomous Robot (STAR) and RAVEN systems being the most reported developments. STAR, in particular, is currently being used in laparoscopic imaging to recognize the surgical site from laparoscopic images and is in the process of establishing an automated suturing system, albeit in animal experiments. The present review examined the possibility of fully autonomous surgical robots in the future.

Advances in deep learning: From diagnosis to treatment

Deep learning has brought about a revolution in the field of medical diagnosis and treatment. The use of deep learning in healthcare has grown exponentially in recent years, achieving physician-level accuracy in various diagnostic tasks and supporting applications such as electronic health records and clinical voice assistants. The emergence of medical foundation models, as a new approach to deep learning, has greatly improved the reasoning ability of machines. Characterized by large training datasets, context awareness, and multi-domain applications, medical foundation models can integrate various forms of medical data to provide user-friendly outputs based on a patien's information. Medical foundation models have the potential to integrate current diagnostic and treatment systems, providing the ability to understand multi-modal diagnostic information and real-time reasoning ability in complex surgical scenarios. Future research on foundation model-based deep learning methods will focus more on the collaboration between physicians and machines. On the one hand, developing new deep learning methods will reduce the repetitive labor of physicians and compensate for shortcomings in their diagnostic and treatment capabilities. On the other hand, physicians need to embrace new deep learning technologies, comprehend the principles and technical risks of deep learning methods, and master the procedures for integrating them into clinical practice. Ultimately, the integration of artificial intelligence analysis with human decision-making will facilitate accurate personalized medical care and enhance the efficiency of physicians.

Surgery in the Next Space Missions

In the coming years, missions to the Moon and Mars shall be the new goals of space flight. The complexity of these missions due to the great distance from Earth and the unforeseen obstacles to settle on another planet have given rise to great concerns for crew health and survival. The need for advanced crew autonomy and a different approach to surgical emergency require new protocols and devices to help future crew medical officers and other crew members in a task of unprecedented difficulty. Hence, the increasing variety of schedules, devices, and protocols being developed. A serious health problem, such as an emerging surgical disease or severe trauma, can jeopardize the mission and survival of the entire crew. Many other difficulties are present in deep-space missions or settlements on other planets, such as communication and supply, also medical, delays, and shortage, and the presence of radiation. Progress in advanced technologies as well as the evolution of robotic surgery and the use of artificial intelligence are other topics of this review. In this particular area of research, even if we are still very far from an "intelligent robot", this evolution must be evaluated in the light of legislative and ethical considerations. This topic was presented at the annual meeting of the American College of Surgeons-Italy Chapter in 2021.

Flexible robotic endoscopy for treating gastrointestinal neoplasms

Therapeutic flexible endoscopic robotic systems have been developed primarily as a platform for endoscopic submucosal dissection (ESD) in the treatment of early-stage gastrointestinal cancer. Since ESD can only be performed by highly skilled endoscopists, the goal is to lower the technical hurdles to ESD by introducing a robot. In some cases, such robots have already been used clinically, but they are still in the research and development stage. This paper outlined the current status of development, including a system by the author’s group, and discussed future challenges.

Innovation in gastrointestinal surgery: the evolution of minimally invasive surgery-a narrative review

Background

Minimally invasive (MI) surgery has revolutionised surgery, becoming the standard of care in many countries around the globe. Observed benefits over traditional open surgery include reduced pain, shorter hospital stay, and decreased recovery time. Gastrointestinal surgery in particular was an early adaptor to both laparoscopic and robotic surgery. Within this review, we provide a comprehensive overview of the evolution of minimally invasive gastrointestinal surgery and a critical outlook on the evidence surrounding its effectiveness and safety.

Methods

A literature review was conducted to identify relevant articles for the topic of this review. The literature search was performed using Medical Subject Heading terms on PubMed. The methodology for evidence synthesis was in line with the four steps for narrative reviews outlined in current literature. The key words used were minimally invasive, robotic, laparoscopic colorectal, colon, rectal surgery.

Conclusion

The introduction of minimally surgery has revolutionised patient care. Despite the evidence supporting this technique in gastrointestinal surgery, several controversies remain. Here we discuss some of them; the lack of high level evidence regarding the oncological outcomes of TaTME and lack of supporting evidence for robotic colorectalrectal surgery and upper GI surgery. These controversies open pathways for future research opportunities with RCTs focusing on comparing robotic to laparoscopic with different primary outcomes including ergonomics and surgeon comfort.

Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method

In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. There are many variants of the APF algorithm, among which the most widely used variants is the velocity potential field (VPF) algorithm. However, the traditional VPF algorithm has inherent defects and problems, such as easily falling into local minimum, being unable to reach the target, poor dynamic obstacle avoidance ability, and safety and efficiency problems. Therefore, this work presents the improved velocity potential field (IVPF) algorithm, which considers direction factors, obstacle velocity factor, and tangential velocity. When encountering dynamic obstacles, the IVPF algorithm can avoid obstacles better to ensure the safety of both the human and robot arm. The IVPF algorithm also does not easily fall into a local problem when encountering different obstacles. The experiments informed the RRT* algorithm, VPF algorithm, and IVPF algorithm for comparison. Compared with the informed RRT* and VPF algorithm, the result of experiments indicate that the performances of the IVPF algorithm have significant improvements when dealing with different obstacles. The main aim of this paper is to provide a safe and efficient path planning algorithm for the robot arm in the medical field. The proposed algorithm can ensure the safety of both the human and the robot arm when the medical and surgical robot arm is working, and enables the robot arm to cope with emergencies and perform tasks better. The application of the proposed algorithm could make the collaborative robots work in a flexible and safe condition, which could open up new opportunities for the future development of medical and surgical scenarios.

Design and validation of looping assistance methods in robotic-assisted neonatal surgical suturing in a chest model

BACKGROUND

Neonate patients have a reduced thoracic cavity, making thoracoscopic procedures even more challenging than their adult counterparts.

METHODS

We evaluated five control strategies for robot-assisted thoracoscopic surgical looping in simulations and experiments with a physical robotic system in a neonate surgical phantom. The strategies are composed of state-of-the-art constrained optimization and a novel looping force feedback term.

RESULTS

All control strategies allowed users to successfully perform looping. A user study in simulation showed that the proposed strategy was superior in terms of Physical demand p < 0.05 $\left(p< 0.05\right)$ and task duration p < 0.05 $\left(p< 0.05\right)$ . The cumulative sum analysis of inexperienced users shows that the proposed looping force feedback can speed up the learning. Results with surgeons did not show a significant difference among control strategies.

CONCLUSIONS

Assistive strategies in looping show promise and further work is needed to extend these benefits to other subtasks in robot-aided surgical suturing.

Self-folding soft-robotic chains with reconfigurable shapes and functionalities

Magnetic continuum soft robots can actively steer their tip under an external magnetic field, enabling them to effectively navigate in complex in vivo environments and perform minimally invasive interventions. However, the geometries and functionalities of these robotic tools are limited by the inner diameter of the supporting catheter as well as the natural orifices and access ports of the human body. Here, we present a class of magnetic soft-robotic chains (MaSoChains) that can self-fold into large assemblies with stable configurations using a combination of elastic and magnetic energies. By pushing and pulling the MaSoChain relative to its catheter sheath, repeated assembly and disassembly with programmable shapes and functions are achieved. MaSoChains are compatible with state-of-the-art magnetic navigation technologies and provide many desirable features and functions that are difficult to realize through existing surgical tools. This strategy can be further customized and implemented for a wide spectrum of tools for minimally invasive interventions.

Optical Coherence Tomography Angiography of the Intestine: How to Prevent Motion Artifacts in Open and Laparoscopic Surgery?

(1) Introduction. The problem that limits the intraoperative use of OCTA for the intestinal circulation diagnostics is the low informative value of OCTA images containing too many motion artifacts. The aim of this study is to evaluate the efficiency and safety of the developed unit for the prevention of the appearance of motion artifacts in the OCTA images of the intestine in both open and laparoscopic surgery in the experiment; (2) Methods. A high-speed spectral-domain multimodal optical coherence tomograph (IAP RAS, Russia) operating at a wavelength of 1310 nm with a spectral width of 100 μm and a power of 2 mW was used. The developed unit was tested in two groups of experimental animals—on minipigs (group I, n = 10, open abdomen) and on rabbits (group II, n = 10, laparoscopy). Acute mesenteric ischemia was modeled and then 1 h later the small intestine underwent OCTA evaluation. A total of 400 OCTA images of the intact and ischemic small intestine were obtained and analyzed. The quality of the obtained OCTA images was evaluated based on the score proposed in 2020 by the group of Magnin M. (3) Results. Without stabilization, OCTA images of the intestine tissues were informative only in 32–44% of cases in open surgery and in 14–22% of cases in laparoscopic surgery. A vacuum bowel stabilizer with a pressure deficit of 22–25 mm Hg significantly reduced the number of motion artifacts. As a result, the proportion of informative OCTA images in open surgery increased up to 86.5% (Χ2 = 200.2, p = 0.001), and in laparoscopy up to 60% (Χ2 = 148.3, p = 0.001). (4) Conclusions. The used vacuum tissue stabilizer enabled a significant increase in the proportion of informative OCTA images by significantly reducing the motion artifacts.

Artificial intelligence and automation in endoscopy and surgery

Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and therapeutic interventions, the digitization of the procedural toolset enables video data capture of the internal human anatomy at unprecedented levels. Interventional video data encapsulate functional and structural information about a patient's anatomy as well as events, activity and action logs about the surgical process. This detailed but difficult-to-interpret record from endoscopic procedures can be linked to preoperative and postoperative records or patient imaging information. Rapid advances in artificial intelligence, especially in supervised deep learning, can utilize data from endoscopic procedures to develop systems for assisting procedures leading to computer-assisted interventions that can enable better navigation during procedures, automation of image interpretation and robotically assisted tool manipulation. In this Perspective, we summarize state-of-the-art artificial intelligence for computer-assisted interventions in gastroenterology and surgery.

Effect of annualized surgeon volume on major surgical complications for abdominal and laparoscopic radical hysterectomy for cervical cancer in China, 2004-2016: a retrospective cohort study

BACKGROUND

Previous studies have suggested that higher surgeon volume leads to improved perioperative outcomes for oncologic surgery; however, the effect of surgeon volumes on surgical outcomes might differ according to the surgical approach used. This paper attempts to evaluate the effect of surgeon volume on complications or cervical cancer in an abdominal radical hysterectomy (ARH) cohort and laparoscopic radical hysterectomy (LRH) cohort.

METHODS

We conducted a population-based retrospective study using the Major Surgical Complications of Cervical Cancer in China (MSCCCC) database to analyse patients who underwent radical hysterectomy (RH) from 2004 to 2016 at 42 hospitals. We estimated the annualized surgeon volumes in the ARH cohort and in the LRH cohort separately. The effect of the surgeon volume of ARH or LRH on surgical complications was examined using multivariable logistic regression models.

RESULTS

In total, 22,684 patients who underwent RH for cervical cancer were identified. In the abdominal surgery cohort, the mean surgeon case volume increased from 2004 to 2013 (3.5 to 8.7 cases) and then decreased from 2013 to 2016 (8.7 to 4.9 cases). The mean surgeon case volume number of surgeons performing LRH increased from 1 to 12.1 cases between 2004 and 2016 (P < 0.01). In the abdominal surgery cohort, patients treated by intermediate-volume surgeons were more likely to experience postoperative complications (OR = 1.55, 95% CI = 1.11-2.15) than those treated by high-volume surgeons. In the laparoscopic surgery cohort, surgeon volume did not appear to influence the incidence of intraoperative or postoperative complications (P = 0.46; P = 0.13).

CONCLUSIONS

The performance of ARH by intermediate-volume surgeons is associated with an increased risk of postoperative complications. However, surgeon volume may have no effect on intraoperative or postoperative complications after LRH.

Asymmetric figure-of-eight single-layer suture technique for intestinal anastomosis: A preliminary study

Background

Anastomotic leakage is a life-threatening complication. Improvement of the anastomosis technique is needed, especially in patients with an inflamed edematous intestine. The aim of our study was to evaluate the safety and efficacy of an asymmetric figure-of-eight single-layer suture technique for intestinal anastomosis in pediatric patients.

Methods

A total of 23 patients underwent intestinal anastomosis at the Department of Pediatric Surgery of Binzhou Medical University Hospital. Demographic characteristics, laboratory parameters, anastomosis time, duration of nasogastric tube placement, day of first postoperative bowel movement, complications, and length of hospital stay were statistically analyzed. The follow-up was conducted for 3-6 months after discharge.

Results

Patients were divided into two groups: the single-layer asymmetric figure-of-eight suture technique (group 1) and the traditional suture technique (group 2). Body mass index in group 1 was lower than in group 2 (14.43 ± 3.23 vs. 19.38 ± 6.74; P = 0.036). The mean intestine anastomosis time in group 1 (18.83 ± 0.83 min) was less than that in group 2 (22.70 ± 4.11 min; P = 0.005). Patients in group 1 had an earlier first postoperative bowel movement (2.17 ± 0.72 vs. 2.80 ± 0.42; P = 0.023). The duration of nasogastric tube placement in group 1 was shorter than that in group 2 (4.12 ± 1.42 vs. 5.60 ± 1.57; P = 0.043). There was no significant difference in laboratory variables, complication occurrence, and length of hospital stay between the two groups.

Conclusion

The asymmetric figure-of-eight single-layer suture technique for intestinal anastomosis was feasible and effective. More studies are needed to compare the novel technique with the traditional single-layer suture.

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Background

A considerable number of recent research have used artificial intelligence (AI) in the area of colorectal cancer (CRC). Surgical treatment of CRC still remains the most important curative component. Artificial intelligence in CRC surgery is not nearly as advanced as it is in screening (colonoscopy), diagnosis and prognosis, especially due to the increased complexity and variability of structures and elements in all fields of view, as well as a general shortage of annotated video banks for utilization.

Methods

A literature search was made and relevant studies were included in the minireview.

Results

The intraoperative steps which, at this moment, can benefit from AI in CRC are: phase and action recognition, excision plane navigation, endoscopy control, real-time circulation analysis, knot tying, automatic optical biopsy and hyperspectral imaging. This minireview also analyses the current advances in robotic treatment of CRC as well as the present possibility of automated CRC robotic surgery.

Conclusions

The use of AI in CRC surgery is still at its beginnings. The development of AI models capable of reproducing a colorectal expert surgeon's skill, the creation of large and complex datasets and the standardization of surgical colorectal procedures will contribute to the widespread use of AI in CRC surgical treatment.

Robots to Improve Surgery for All

Surgeons around the world are now using robot-assisted tech to help them perform minimally invasive operations ranging from hernia repair and gall bladder removal to knee replacement and cancer-related colectomy, often manipulating the surgical tools from a computer console some distance from the patient. With names like da Vinci, Aquabeam, and Mako, robotic surgical technologies are becoming more common. As an example, industry powerhouse Intuitive reported in late 2021 that the number of surgical procedures using its robotic da Vinci system had topped 10 million globally [1].

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.

Deep point cloud landmark localization for fringe projection profilometry

Point clouds have been widely used due to their information being richer than images. Fringe projection profilometry (FPP) is one of the camera-based point cloud acquisition techniques that is being developed as a vision system for robotic surgery. For semi-autonomous robotic suturing, fluorescent fiducials were previously used on a target tissue as suture landmarks. This not only increases system complexity but also imposes safety concerns. To address these problems, we propose a numerical landmark localization algorithm based on a convolutional neural network (CNN) and a conditional random field (CRF). A CNN is applied to regress landmark heatmaps from the four-channel image data generated by the FPP. A CRF leveraging both local and global shape constraints is developed to better tune the landmark coordinates, reject extra landmarks, and recover missing landmarks. The robustness of the proposed method is demonstrated through ex vivo porcine intestine landmark localization experiments.

Autonomous robotic surgery makes light work of anastomosis

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