As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons' skills in performing procedures by robotic surgery This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments The tracking method was applied to 54 video segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis The RMSE was 352 mm, the AUC of 1 mm, 2 mm, and 5 mm were 07, 078, and 086, respectively, and Pearson's correlation coefficients were 09 on the x-axis and 087 on the y-axis The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS) The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method

Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations.

Augmented reality (AR) is an innovative system that enhances the real world by superimposing virtual objects on reality. The aim of this study was to analyze the application of AR in medicine and which of its technical solutions are the most used. We carried out a scoping review of the articles published between 2019 and February 2022. The initial search yielded a total of 2649 articles. After applying filters, removing duplicates and screening, we included 34 articles in our analysis. The analysis of the articles highlighted that AR has been traditionally and mainly used in orthopedics in addition to maxillofacial surgery and oncology. Regarding the display application in AR, the Microsoft HoloLens Optical Viewer is the most used method. Moreover, for the tracking and registration phases, the marker-based method with a rigid registration remains the most used system. Overall, the results of this study suggested that AR is an innovative technology with numerous advantages, finding applications in several new surgery domains. Considering the available data, it is not possible to clearly identify all the fields of application and the best technologies regarding AR.

/pdf/augmented-reality-in-surgery-a-scoping-review-11q7cydr.pdf

Augmented Reality in Surgery: A Scoping Review

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human–robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future.

/pdf/augmented-reality-ar-for-surgical-robotic-and-autonomous-2rq7qpy2.pdf

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions

Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.

/pdf/using-deep-learning-to-identify-the-recurrent-laryngeal-rw7pst6izm.pdf

Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy

Feasibility of the application of mixed reality in mandible reconstruction with fibula flap: A cadaveric specimen study

We adopted a vision-based tracking system for augmented reality (AR), and evaluated whether it helped surgeons to localize the recurrent laryngeal nerve (RLN) during robotic thyroid surgery. We constructed an AR image of the trachea, common carotid artery, and RLN using CT images. During surgery, an AR image of the trachea and common carotid artery were overlaid on the physical structures after they were exposed. The vision-based tracking system was activated so that the AR image of the RLN followed the camera movement. After identifying the RLN, the distance between the AR image of the RLN and the actual RLN was measured. Eleven RLNs (9 right, 4 left) were tested. The mean distance between the RLN AR image and the actual RLN was 1.9 ± 1.5 mm (range 0.5 to 3.7). RLN localization using AR and vision-based tracking system was successfully applied during robotic thyroidectomy. There were no cases of RLN palsy. This technique may allow surgeons to identify hidden anatomical structures during robotic surgery.

/pdf/vision-based-tracking-system-for-augmented-reality-to-45t7i919vm.pdf

Vision-based tracking system for augmented reality to localize recurrent laryngeal nerve during robotic thyroid surgery

Background and objectives: Currently, few studies have been conducted on postoperative chylothorax, specifically in total thyroidectomy with modified radical neck dissection (MRND) in papillary thyroid carcinoma patients. This study provides the actual incidence, etiology, and clinical features of postoperative chylothorax and reports the clinical outcomes after treatment, which were dependent upon the severity of the complications. Materials and Methods: The medical charts of 111 papillary thyroid cancer (PTC) patients who underwent total thyroidectomy with modified radical neck dissection from January 2016 to December 2018 were reviewed retrospectively. The results were compared in three groups: the no chylothorax group, the subclinical (asymptomatic) group, and the clinical (symptomatic) group. Results: Chylothorax occurred in 23 patients (20.7%, 23/111). Nineteen (82.6%, 19/23) were subclinical chylothorax cases, which implies a small amount of chyle leakage with no respiratory symptoms. Four (17.4%, 4/23) were clinical, meaning they had either respiratory symptoms, such as dyspnea, desaturation, or a large amount of chylothorax in the images. The incidence was significantly higher in patients who underwent left modified radical neck dissection, and this corresponds to the side in which chylothorax occurred. There were also statistical differences in the drainage color, peak amount, or drain removal time. Conclusions: Postoperative chylothorax is a rare complication following neck dissection. However, it can be fatal if the condition progresses. Therefore, patients who undergo total thyroidectomy with modified radical neck dissection, especially on the left side, should be monitored for respiratory symptoms, and serial chest x-ray images obtained.

Postoperative Chylothorax after Modified Radical Neck Dissection for Thyroid Carcinoma: A Missable Rare Complication of Thyroid Surgery.

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Vision-based tracking system for augmented reality to localize recurrent laryngeal nerve during robotic thyroid surgery

Postoperative Chylothorax after Modified Radical Neck Dissection for Thyroid Carcinoma: A Missable Rare Complication of Thyroid Surgery.