BACKGROUND: The association between surgical performance ratings and clinical outcomes in robotic surgery is poorly understood. Additionally, no studies have reported on the relationship between the surgeon\'s initial case-skill evaluation and the learning curve in robot-assisted surgery. We evaluated whether an objective surgical technique evaluation score for initial robot-assisted radical prostatectomy (RARP) was associated with clinical outcomes and surgeons\' learning curves.
METHODS: Six surgeons who were trained in and started to perform RARP at our institution were included. Anonymized, unedited videos of each surgeon\'s 10th RARP case were evaluated by three reviewers, using modified Objective Structured Assessment of Technical Skill (OSATS) scores. We then divided the surgeons into two groups on the basis of these OSATS scores. We retrospectively compared the clinical outcomes and learning curves of the console time of the two groups for consecutive RARPs, performed from March 2018 to July 2023.
RESULTS: We analyzed 258 RARPs (43 cases/surgeon), including 129 cases performed by high-OSATS score surgeons (18.2-19.3 points) and 129 cases performed by low-OSATS score surgeons (11.9-16.0 points). Overall, the high-OSATS score group had significantly shorter operation and console times than the low-OSATS score group did (both P < 0.01) and their patients\' rate of continence recovery by 3 months post-RARP was significantly higher (P = 0.03). However, complications, blood loss, and positive margins did not differ between the groups (P = 0.08, P = 0.51, and P = 0.90, respectively). The high-OSATS score group had a significantly shorter console time than the low-OSATS score group did after the 11-20 cases.
CONCLUSIONS: The OSATS score in early RARP cases can predict subsequent surgical outcomes and surgeons\' learning curves.

UNASSIGNED: Complexities of robotic distal gastrectomy (RDG) give reason to assess physician\'s surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments.
UNASSIGNED: Fifty-five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi-stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non-experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed.
UNASSIGNED: We annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument\'s usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non-experienced group.
UNASSIGNED: This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill.

Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the \"respect for tissue.\" The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety.

Surgical education has seen immense change recently. Increased demand for iterative evaluation of trainees from medical school to independent practice has led to the generation of an overwhelming amount of data related to an individual\'s competency. Artificial intelligence has been proposed as a solution to automate and standardize the ability of stakeholders to assess the technical and nontechnical abilities of a surgical trainee. In both the simulation and clinical environments, evidence supports the use of machine learning algorithms to both evaluate trainee skill and provide real-time and automated feedback, enabling a shortened learning curve for many key procedural skills and ensuring patient safety.

OBJECTIVE: This study aimed to compare two methods of feedback: verbal face-to-face feedback after direct observation (F2F-feedback) versus electronic-written feedback after observation of recorded-VDO of student\'s performance (VDO-feedback), in terms of effectiveness in improving skill, effects on motivation and satisfaction.
BACKGROUND: Medical schools are responsible for teaching and ensuring proficiency of basic surgical skills. Feedback is effective in developing psychomotor skills; by providing information of learner\'s current performance, how to improve, and enhancing motivation.
METHODS: Fifty-eight medical students (3rd- 4th year) were trained to perform vertical mattress suture in small groups. Then, during 6-week period of self-directed practice, students were randomized into group1 VDO-feedback (male:female = 21:8) and group 2 F2F-feedback (male:female = 20:9). Feedbacks were provided once every 2 weeks (Week2, Week4). End-of-rotation OSCE was at Week6, and retention tested was at Week8. Performance checklist (Cronbach\'s Alpha 0.72) was used to assess skill at 4 timepoints; pre- and post- small group learning, OSCE, and retention phase. Questionnaire was used to assess motivation, learning strategies and satisfaction (Cronbach\'s Alpha 0.83).
RESULTS: After in-class learning, further significant improvement of skills could be gained by both F2F- and VDO- feedbacks (p < 0.0001). Both could similarly retain skill for at least 4 weeks later without additional practice. Self-efficacy, test anxiety, and cognitive strategies scores were significantly increased in both groups (p < 0.05). Extrinsic motivation was increased in VDO-feedback group. No difference in satisfaction between groups was observed.
CONCLUSIONS: VDO-feedback could be alternative to F2F-feedbacks for basic surgical skill training when limitation for simultaneous meeting of teacher and students occurs.
BACKGROUND: This study has been registered to Thai Clinical Trial Registry (WHO International Clinical Trial Registry Platform) on 11/07/2023 (TCTR20230711005).

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

Cardiac paraganglioma is a kind of rare neuroendocrine tumor characterized by the persistent secretion of catecholamines. Under excessive exposure of catecholamines, some atypical symptoms are presented, including hypertension, arrhythmias, and headache. The case of surgical treatment of a 28-year-old woman with primary cardiac paraganglioma is presented for experience sharing and surgical skill improvements.

OBJECTIVE: Surgeons\' skill in the operating room is a major determinant of patient outcomes. Assessment of surgeons\' skill is necessary to improve patient outcomes and quality of care through surgical training and coaching. Methods for video-based assessment of surgical skill can provide objective and efficient tools for surgeons. Our work introduces a new method based on attention mechanisms and provides a comprehensive comparative analysis of state-of-the-art methods for video-based assessment of surgical skill in the operating room.
METHODS: Using a dataset of 99 videos of capsulorhexis, a critical step in cataract surgery, we evaluated image feature-based methods and two deep learning methods to assess skill using RGB videos. In the first method, we predict instrument tips as keypoints and predict surgical skill using temporal convolutional neural networks. In the second method, we propose a frame-wise encoder (2D convolutional neural network) followed by a temporal model (recurrent neural network), both of which are augmented by visual attention mechanisms. We computed the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values through fivefold cross-validation.
RESULTS: To classify a binary skill label (expert vs. novice), the range of AUC estimates was 0.49 (95% confidence interval; CI = 0.37 to 0.60) to 0.76 (95% CI = 0.66 to 0.85) for image feature-based methods. The sensitivity and specificity were consistently high for none of the methods. For the deep learning methods, the AUC was 0.79 (95% CI = 0.70 to 0.88) using keypoints alone, 0.78 (95% CI = 0.69 to 0.88) and 0.75 (95% CI = 0.65 to 0.85) with and without attention mechanisms, respectively.
CONCLUSIONS: Deep learning methods are necessary for video-based assessment of surgical skill in the operating room. Attention mechanisms improved discrimination ability of the network. Our findings should be evaluated for external validity in other datasets.

暂无摘要。

OBJECTIVE: Published studies repeatedly demonstrate an advantage of three-dimensional (3D) laparoscopic surgery over two-dimensional (2D) systems but with quite heterogeneous results. This raises the question whether clinics must replace 2D technologies to ensure effective training of future surgeons.
METHODS: We recruited 45 students with no experience in laparoscopic surgery and comparable characteristics in terms of vision and frequency of video game usage. The students were randomly allocated to 3D (n = 23) or 2D (n = 22) groups and performed 10 runs of a laparoscopic \"peg transfer\" task in the Luebeck Toolbox. A repeated-measures ANOVA for operation times and a generalized linear mixed model for error rates were calculated. The main effects of laparoscopic condition and run, as well as the interaction term between the two, were examined.
RESULTS: No statistically significant differences in operation times and error rates were observed between 2D and 3D groups (p = 0.10 and p = 0.72, respectively). The learning curve showed a significant reduction in operation time and error rates (both p\'s < 0.001). No significant interactions between group and run were detected (operation time: p = 0.342, error rates: p = 0.83). With respect to both endpoints studied, the learning curves reached their plateau at the 7th run.
CONCLUSIONS: The result of our study with laparoscopic novices revealed no significant difference between 2D and 3D technology with respect to performance time and the error rate in a simple standardized test. In the future, surgeons may thus still be trained in both techniques.