Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field

Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills and high inter-annotator consistency. Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF. The neural network is jointly trained with a supervised regression loss and an unsupervised rank loss. In experiments, the proposed method achieves 0.55 Spearman's correlation with the ground truth of overall technical skill, which is even comparable with the human performance of junior surgeons.Comment: MICCAI 201

Video Based Assessment of OSATS Using Sequential Motion Textures

Presented at the Fifth Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI)We present a fully automated framework for video based surgical skill assessment that incorporates the sequential and qualitative aspects of surgical motion in a data-driven manner. We replicate Objective Structured Assessment of Technical Skills (OSATS) assessments, which provides both an overall and in-detail evaluation of basic suturing skills required for surgeons. Video analysis techniques are introduced that incorporate sequential motion aspects into motion textures. We also demonstrate significant performance improvements over standard bag-of-words and motion analysis approaches. We evaluate our framework in a case study that involved medical students with varying levels of expertise performing basic surgical tasks in a surgical training lab setting.Intuitive Surgica

Generalized and efficient skill assessment from IMU data with applications in gymnastics and medical training

Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work

Advances in automated surgery skills evaluation

Training a surgeon to be skilled and competent to perform a given surgical procedure, is an important step in providing a high quality of care and reducing the risk of complications. Traditional surgical training is carried out by expert surgeons who observe and assess the trainees directly during a given procedure. However, these traditional training methods are time-consuming, subjective, costly, and do not offer an overall surgical expertise evaluation criterion. The solution for these subjective evaluation methods is a sensor-based methodology able to objectively assess the surgeon's skill level. The development and advances in sensor technologies enable capturing and studying the information obtained from complex surgery procedures. If the surgical activities that occur during a procedure are captured using a set of sensors, then the skill evaluation methodology can be defined as a motion and time series analysis problem. This work aims at developing machine learning approaches for automated surgical skill assessment based on hand motion analysis. Specifically, this work presents several contributions to the field of objective surgical techniques using multi-dimensional time series, such as 1) introduce a new distance measure for the surgical activities based on the alignment of two multi-dimensional time series, 2) develop an automated classification framework to identify the surgeon proficiency level using wrist worn sensors, 3) develop a classification technique to identify elementary surgical tasks: suturing, needle passing, and knot tying , 4) introduce a new surgemes mean feature reduction technique which help improve the machine learning algorithms, 5) develop a framework for surgical gesture classification by employing the mean feature reduction method, 6) design an unsupervised method to identify the surgemes in a given procedure.Includes bibliographical references

Automated surgical OSATS prediction from videos

The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices, and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve statistically significant correlation (p-valu