The authors regret that an affiliation was omitted for author Dehlela Shabir. The correct affiliation details are as follows: Dehlela Shabir a,b,1 a Department of Surgery, Hamad Medical Corporation, Doha, Qatar b Computer Science and Engineering Department, Qatar University, Doha, Qatar. The authors would like to apologise for any inconvenience caused.

Surgical skills proficiency lowers the incidence of adverse clinical outcomes during surgeries. Artificial intelligence (AI) has been applied for surgical skills assessment, especially in the field of minimally invasive surgeries (MIS). This paves the way for integrating AI for skills assessment in open surgeries as well. An overview of its applications can inform the scientific community and facilitate further developments. In this scoping review , we present the open surgeries and clinical settings where AI-based skill assessment has been applied, the kind of surgical data acquired for the AI-based algorithms, and the types of AI-based models used for automated skills assessment. A total of 40 articles were identified and included. Majority of the articles focused on macrosurgical suturing (45 %, n = 18). Most of the studies acquired data by capturing surgeon's hands (50 %, n = 20). About 35 % utilized deep learning algorithms , specifically convolutional neural networks (CNN) ( n = 14). The assessment input for the automation algorithms were predominantly hand movement. Around 37.5 % ( n = 15) of the studies assessed algorithm performance using classification accuracy . In the review, we compare conventional methods such as statistical modeling and custom algorithms with the emerging AI-based approaches. We also explore the utilization of object detection and temporal information for surgical skills assessment. We highlight the progress in automated skills assessment during open surgery with advancements in sensor technology, and AI algorithms with high prediction accuracies. Further developments in data acquisition and processing methods are essential to facilitate clinical implementation of such technologies.

Robotic scope assistant systems allow surgeons to adjust the operative field view during surgery by robotically maneuvering laparoscopes. A Human-Robot Interface (HRI) is used for issuing commands to these systems, with an interaction mode mapping these commands to laparoscope movements. Optimizing the HRI and interaction mode can streamline laparoscope positioning as well as reduce cognitive workload, helping the surgeon focus on the surgical procedure. Comparing and assessing various HRIs and interaction modes is essential for efficient laparoscope maneuvering. This study evaluates HRIs based on head-motion, eye-motion, hand-motion, and voice-input operating under three interaction modes (namely: discrete, continuous, and threshold). The participants performed a user study comparing different HRIs under two simulated surgical scenarios (one in a real environment and the other in a virtual environment). The results indicated that head and eye-based HRIs performed well in continuous interaction mode, while the voice-based interface suffered from a delay. Conversely, hand-based HRIs demonstrated superior performance in both scenarios across all evaluation parameters. The study provides a benchmark for the comparison of different HRIs and provides insights into the effectiveness, limitations, and potential advantages of different HRIs.

The work assesses the efficacy of computer based remote tele-mentoring system (i.e. when the mentor and mentee are physically separated) for teaching minimally invasive surgical skills. The visual cues used for tele-mentoring comprises real-time virtual surgical instruments' motion augmented onto the operative field and remotely controlled by the mentor. In the feasibility study, the surgical task of laparoscopic intracorporeal suturing was simulated among 18 mentor-mentee pairs. Three modes of mentoring were used. Mode-I included traditional learning using pre-recorded videos (in absence of a mentor). Mode-II used traditional in-person hands-on mentoring. In Mode-III, a tele-mentoring prototype was used that connected a mentee with a remote mentor. Error count and duration were recorded for a learning stage followed by a testing stage for the three modes. The results show the error count for Mode-III reduces significantly as compared to Mode-I in the learning stage. Similarly, the error count for Mode-III also reduces significantly as compared to Mode-I in the testing stage. The errors count for Mode-III were equivalent to that of Mode-II for both learning and teaching stages. Furthermore, in Mode-III the duration reduces from learning to testing stage exhibiting the learning effect. Thus, computer based remote tele-mentoring is effective and more convenient to demonstrate surgical sub-steps consisting of tool-tissue interaction facilitating surgical skill transfer.

In a telementoring setup for open surgeries, the motion of virtual surgical instruments is superimposed onto the operative field. This assists a mentor to effectively convey to the mentee the information pertaining to the required tool-tissue interactions during the surgery. The aim of this work is to assess the effects of using a virtual reality environment at mentor's site (in contrast to conventional visualization on a two-dimensional screen) during surgical telementoring. A user study is conducted simulating motion of virtual surgical instrument in an open surgery. The results show that mentor is able to demonstrate with higher accuracy and in shorter duration, the required virtual surgical instrument motions. Thus, rendering information to the mentor in an immersive virtual reality environment assists in better understanding of the operative field and enhanced control of the virtual surgical instrument motion. This further aids in conveying accurate information pertaining to the tool-tissue interaction to the mentee.

The advent of intraoperative real-time image guidance has led to the emergence of new surgical interventional paradigms including image-guided robot assistance. Most often the use of an intraoperative imaging modality is limited to visual perception of the area of procedure. In this work, we propose a system for performing interventions with real-time Magnetic Resonance Imaging (rtMRI). The described computational core, processes on-the-fly rtMRI and generates dynamic guidance virtual fixture that in turn is used to update visualization and a force-feedback interface. The system was experimentally tested by applying it to a simulated Transapical Aortic Valve Implantation with a virtual robotic manipulator. The study results demonstrate significant improvement in the surgical task by decreasing the duration of the procedure and increasing safety in the presence of cardiac and breathing motion.

Telementoring in surgery facilitates the transfer of surgical knowledge from the mentor to the mentee. Augmented Reality (AR) further assists this transfer by overlaying visual cues (e.g., in the form of virtual surgical instrument motion) generated by the mentor onto the operative field of the mentee. In this work, we present a benchmark for comparing such AR based surgical telementoring systems. The results compare the network performances of these systems across different types of surgery (open or minimally invasive), based on the locations of the mentor and the mentee (inter- or intra- country), and finally the underlying networking protocols (RTMP versus WebRTC).

The delineation of liver difficult due to its similar intensity distributions in CT images. Additionally, there have been other challenges such that the variability in shape, size, and proximity to the other neighboring organs. The blurred liver edges and low contrast on the CT image make the segmentation further challenging. Furthermore, the patient movement during CT data acquisition along with spatial averaging lead to reconstruction artifacts; these are all reflected on the CT image complicating the segmentation task. In this paper, we have proposed a UNet-based automatic liver segmentation approach to delineate the boundaries between the liver and other abdominal organs. The algorithm is tested on publicly available datasets. The average values of Dice similarity coefficient (DC), Relative absolute volume difference (RAVD), Average symmetric surface distance (ASSD), Maximum symmetric surface distance (MSSD), Hausdorff distance (HD), and Precision are found to be 0.95±0.02, 0.04±0.02, 1.03±0.39, 1.15±0.5, 2.85±1.89, and 0.91±0.12, respectively.

Text Summarization is the process of generating a concise and meaningful summary of a text. To better help identify relevant information and consume relevant information faster, automatic text summarizing methods are needed to address the growing amount of text data available online. Text Summarization techniques are classified into abstractive and extractive summarization. The extractive summarization technique focuses on important information like sentences or phrases which are extracted from a given text file or original document and stack them together to create a summary. In this study, we review and compare the performance of three extraction-based summarization techniques which are Conceptual method, Text Rank and Sentence Scoring. Furthermore, we evaluate the quality of summarization by comparing individual methods on unsummarized text with their corresponding human made gold standard summaries.

This work presents an interventional planning software to be used in conjunction with a robotic manipulator to perform transrectal MR guided prostate biopsies. The interventional software was designed taking in consideration a generic manipulator used under the two modes of operation: side-firing and end-firing of the biopsy needle. Studies were conducted with urologists using the software to plan virtual biopsies. The results show features of software relevant for operating efficiently under the two modes of operation.

Prostate biopsy is considered as a definitive way for diagnosing prostate malignancies. Urologists are currently moving towards MR-guided prostate biopsies over conventional transrectal ultrasound-guided biopsies for prostate cancer detection. Recently, robotic systems have started to emerge as an assistance tool for urologists to perform MR-guided prostate biopsies. However, these robotic assistance systems are designed for a specific clinical environment and cannot be adapted to modifications or changes applied to the clinical setting and/or workflow. This work presents the preliminary design of a cable-driven manipulator developed to be used in both MR scanners and MR-ultrasound fusion systems. The proposed manipulator design and functionality are evaluated on a simulated virtual environment. The simulation is created on an in-house developed interventional planning software to evaluate the ergonomics and usability. The results show that urologists can benefit from the proposed design of the manipulator and planning software to accurately perform biopsies of targeted areas in the prostate.

Needle-based image-guided interventions (NB-IGI) are well established practices that are rapidly expanding to a wider range of therapeutic and diagnostic interventions due to their impact on clinical outcomes. In parallel, a number of robotic manipulators are emerging to increase accuracy, decrease inter-operator variabilities, and reduce the duration of the procedure. Most current systems are application-specific, thereby are appropriate for a limited number of procedures and further exacerbating the already rising costs of healthcare. In order to expand clinical usage of robotic NB-IGI, and eventually reduce the burden on healthcare costs, this paper proposes a modular, customizable robotic systems concept. The concept includes simplified hardware and control techniques, and initial results demonstrate that accuracy is within clinically acceptable limits. Preliminary designs, implementations, and assessments of the proposed system demonstrate its potential and clinical value.

Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is attached. Although commercial computer-assisted navigation systems exist to guide the placement of these tunnels, most of them are limited to a fixed pose without due consideration of dynamic factors involved in different knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error particularly in the ligament attachment area. The method uses 3D preoperative data at different flexion angles to build a subject-specific statistical model of knee pose. To circumvent the problem of limited training samples and ensure physically meaningful pose instantiation, homogeneous transformations between different poses and local-deformation finite element modelling are used to enlarge the training set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the subject-specific flexion characteristics. The advantages of the method were also tested by detailed comparison to standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement, and other state-of-the-art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical value.

In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem due to a variety of complications including significant noise attributed to bleeding and smoke from cutting, poor appearance contrast between different tissue types, occluding surgical tools, and limited visibility of the objects' geometries on the projected camera views. In this paper, we propose a multi-modal approach to segmentation where preoperative 3D computed tomography scans and intraoperative stereo-endoscopic video data are jointly analyzed. The idea is to segment multiple poorly visible structures in the stereo/multichannel endoscopic videos by fusing reliable prior knowledge captured from the preoperative 3D scans. More specifically, we estimate and track the pose of the preoperative models in 3D and consider the models' non-rigid deformations to match with corresponding visual cues in multi-channel endoscopic video and segment the objects of interest. Further, contrary to most augmented reality frameworks in endoscopic surgery that assume known camera parameters, an assumption that is often violated during surgery due to non-optimal camera calibration and changes in camera focus/zoom, our method embeds these parameters into the optimization hence correcting the calibration parameters within the segmentation process. We evaluate our technique on synthetic data, ex vivo lamb kidney datasets, and in vivo clinical partial nephrectomy surgery with results demonstrating high accuracy and robustness.