How can surgical skills in laparoscopic colon surgery be objectively assessed?—a scoping review

Background In laparoscopic colorectal surgery, higher technical skills have been associated with improved patient outcome. With the growing interest in laparoscopic techniques, pressure on surgeons and certifying bodies is mounting to ensure that operative procedures are performed safely and efficiently. The aim of the present review was to comprehensively identify tools for skill assessment in laparoscopic colon surgery and to assess their validity as reported in the literature. Methods A systematic search was conducted in EMBASE and PubMed/MEDLINE in May 2021 to identify studies examining technical skills assessment tools in laparoscopic colon surgery. Available information on validity evidence (content, response process, internal structure, relation to other variables, and consequences) was evaluated for all included tools. Results Fourteen assessment tools were identified, of which most were procedure-specific and video-based. Most tools reported moderate validity evidence. Commonly not reported were rater training, assessment correlation with variables other than training level, and validity reproducibility and reliability in external educational settings. Conclusion The results of this review show that several tools are available for evaluation of laparoscopic colon cancer surgery, but few authors present substantial validity for tool development and use. As we move towards the implementation of new techniques in laparoscopic colon surgery, it is imperative to establish validity before surgical skill assessment tools can be applied to new procedures and settings. Therefore, future studies ought to examine different aspects of tool validity, especially correlation with other variables, such as patient morbidity and pathological reports, which impact patient survival.

Automated recognition of objects and types of forceps in surgical images using deep learning

Analysis of operative data with convolutional neural networks (CNNs) is expected to improve the knowledge and professional skills of surgeons. Identification of objects in videos recorded during surgery can be used for surgical skill assessment and surgical navigation. The objectives of this study were to recognize objects and types of forceps in surgical videos acquired during colorectal surgeries and evaluate detection accuracy. Images (n = 1818) were extracted from 11 surgical videos for model training, and another 500 images were extracted from 6 additional videos for validation. The following 5 types of forceps were selected for annotation: ultrasonic scalpel, grasping, clip, angled (Maryland and right-angled), and spatula. IBM Visual Insights software was used, which incorporates the most popular open-source deep-learning CNN frameworks. In total, 1039/1062 (97.8%) forceps were correctly identified among 500 test images. Calculated recall and precision values were as follows: grasping forceps, 98.1% and 98.0%; ultrasonic scalpel, 99.4% and 93.9%; clip forceps, 96.2% and 92.7%; angled forceps, 94.9% and 100%; and spatula forceps, 98.1% and 94.5%, respectively. Forceps recognition can be achieved with high accuracy using deep-learning models, providing the opportunity to evaluate how forceps are used in various operations.

SD-Net: joint surgical gesture recognition and skill assessment

Purpose Surgical gesture recognition has been an essential task for providing intraoperative context-aware assistance and scheduling clinical resources. However, previous methods present limitations in catching long-range temporal information, and many of them require additional sensors. To address these challenges, we propose a symmetric dilated network, namely SD-Net, to jointly recognize surgical gestures and assess surgical skill levels only using RGB surgical video sequences. Methods We utilize symmetric 1D temporal dilated convolution layers to hierarchically capture gesture clues under different receptive fields such that features in different time span can be aggregated. In addition, a self-attention network is bridged in the middle to calculate the global frame-to-frame relativity. Results We evaluate our method on a robotic suturing task from the JIGSAWS dataset. The gesture recognition task largely outperforms the state of the arts on the frame-wise accuracy up to $$\sim $$ ∼ 6 points and the F1@50 score $$\sim $$ ∼ 8 points. We also keep the 100% predicted accuracy for the skill assessment task using LOSO validation scheme. Conclusion The results indicate that our architecture is able to obtain representative surgical video features by extensively considering the spatial, temporal and relational context from raw video input. Furthermore, the better performance in multi-task learning implies that surgical skill assessment has a complementary effects to gesture recognition task.

Deep neural networks for the assessment of surgical skills: A systematic review

Surgical training in medical school residency programs has followed the apprenticeship model. The learning and assessment process is inherently subjective and time-consuming. Thus, there is a need for objective methods to assess surgical skills. Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency. There is considerable recent interest in deep neural networks (DNNs) due to the availability of powerful algorithms, multiple datasets, some of which are publicly available, as well as efficient computational hardware to train and host them. We have reviewed 530 papers, of which we selected 25 for this systematic review. Based on this review, we concluded that DNNs are potent tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets representing the surgical trainee and expert demographics and multimodal data beyond kinematics and videos.