Kanav Kahol

Effect of short-term pretrial practice on surgical proficiency in simulated environments: a randomized trial of the "preoperative warm-up" effect.

BACKGROUND Surgery is a skill-driven discipline. While other high-stake professions with comparable cognitive and psychomotor skill requirements often use warm-up exercises for achieving better proficiency, the effects of such practice have not been investigated sufficiently in surgical tasks. DESIGN Subjects performed standardized exercises as a preoperative warm-up, after which the standardized exercises were repeated in a randomized order. In a variation to investigate the generalizability of preoperative warm-up, the experimental group was allowed to warm-up with the standardized exercises, after which a different task (electrocautery simulation) was performed. To investigate the effect of warm-up on fatigue, participants were involved in eight sessions (four before night call, four after night call), after which the tasks were repeated. Results were analyzed using ANOVA to plot differences between warm-up and followup condition. RESULTS All outcomes measures demonstrated statistically significant improvements after all of the post-warm-up exercises (p < 0.01), and were seen in all groups with differing experience levels. In addition, the simple warm-up exercises led to a significant increase in proficiency in followup electrocautery task for the experimental group when compared with the control group (p < 0.0001). There was also significant improvement in performance of the fatigued group to approximately baseline performance (p < 0.05), although they were not able to reach their optimal potential performance. CONCLUSION Preoperative warm-up for 15 to 20 minutes with simple surgical exercises leads to a substantial increase in surgical skills proficiency during followup tasks.

Automated gesture segmentation from dance sequences

Complex human motion (e.g. dance) sequences are typically analyzed by segmenting them into shorter motion sequences, called gestures. However, this segmentation process is subjective, and varies considerably from one choreographer to another. Dance sequences also exhibit a large vocabulary of gestures. In this paper, we propose an algorithm called hierarchical activity segmentation. This algorithm employs a dynamic hierarchical layered structure to represent human anatomy, and uses low-level motion parameters to characterize motion in the various layers of this hierarchy, which correspond to different segments of the human body. This characterization is used with a naive Bayesian classifier to derive choreographer profiles from empirical data that are used to predict how particular choreographers segment gestures in other motion sequences. When the predictions were tested with a library of 45 3D motion capture sequences (with 185 distinct gestures) created by 5 different choreographers, they were found to be 93.3% accurate.

Gesture segmentation in complex motion sequences

Complex human motion sequences (such as dances) are typically analyzed by segmenting them into shorter motion sequences, called gestures. However, this segmentation process is subjective, and varies considerably from one human observer to another. In this paper, we propose an algorithm called hierarchical activity segmentation. This algorithm employs a dynamic hierarchical layered structure to represent the human anatomy, and uses low-level motion parameters to characterize motion in the various layers of this hierarchy, which correspond to different segments of the human body. This characterization is used with a naive Bayesian classifier to derive creator profiles from empirical data. Then those profiles are used to predict how creators will segment gestures in other motion sequences. When the predictions were tested with a library of 3D motion capture sequences, which were segmented by 2 choreographers they were found to be reasonably accurate.

A virtual reality simulator for orthopedic basic skills: A design and validation study

Orthopedic drilling as a skill demands high levels of dexterity and expertise from the surgeon. It is a basic skill that is required in many orthopedic procedures. Inefficient drilling can be a source of avoidable medical errors that may lead to adverse events. It is hence important to train and evaluate residents in safe environments for this skill. This paper presents a virtual orthopedic drilling simulator that was designed to provide visiohaptic interaction with virtual bones. The simulation provides a realistic basic training environment for orthopedic surgeons. It contains modules to track and analyze movements of surgeons, in order to determine their surgical proficiency. The simulator was tested with senior surgeons, residents and medical students for validation purposes. Through the multi-tiered testing strategy it was shown that the simulator was able to produce a learning effect that transfers to real-world drilling. Further, objective measures of surgical performance were found to be able to differentiate between experts and novices.

Cognitive simulators for medical education and training

Simulators for honing procedural skills (such as surgical skills and central venous catheter placement) have proven to be valuable tools for medical educators and students. While such simulations represent an effective paradigm in surgical education, there is an opportunity to add a layer of cognitive exercises to these basic simulations that can facilitate robust skill learning in residents. This paper describes a controlled methodology, inspired by neuropsychological assessment tasks and embodied cognition, to develop cognitive simulators for laparoscopic surgery. These simulators provide psychomotor skill training and offer the additional challenge of accomplishing cognitive tasks in realistic environments. A generic framework for design, development and evaluation of such simulators is described. The presented framework is generalizable and can be applied to different task domains. It is independent of the types of sensors, simulation environment and feedback mechanisms that the simulators use. A proof of concept of the framework is provided through developing a simulator that includes cognitive variations to a basic psychomotor task. The results of two pilot studies are presented that show the validity of the methodology in providing an effective evaluation and learning environments for surgeons.

Framework for performance evaluation of face recognition algorithms

Face detection and recognition is becoming increasingly important in the contexts of surveillance,credit card fraud detection,assistive devices for visual impaired,etc. A number of face recognition algorithms have been proposed in the literature.The availability of a comprehensive face database is crucial to test the performance of these face recognition algorithms.However,while existing publicly-available face databases contain face images with a wide variety of poses angles, illumination angles,gestures,face occlusions,and illuminant colors, these images have not been adequately annotated,thus limiting their usefulness for evaluating the relative performance of face detection algorithms. For example,many of the images in existing databases are not annotated with the exact pose angles at which they were taken.In order to compare the performance of various face recognition algorithms presented in the literature there is a need for a comprehensive,systematically annotated database populated with face images that have been captured (1)at a variety of pose angles (to permit testing of pose invariance),(2)with a wide variety of illumination angles (to permit testing of illumination invariance),and (3)under a variety of commonly encountered illumination color temperatures (to permit testing of illumination color invariance). In this paper, we present a methodology for creating such an annotated database that employs a novel set of apparatus for the rapid capture of face images from a wide variety of pose angles and illumination angles. Four different types of illumination are used,including daylight,skylight,incandescent and fluorescent. The entire set of images,as well as the annotations and the experimental results,is being placed in the public domain,and made available for download over the worldwide web.

Toward automated workflow analysis and visualization in clinical environments

Lapses in patient safety have been linked to unexpected perturbations in clinical workflow. The effectiveness of workflow analysis becomes critical to understanding the impact of these perturbations on patient outcome. The typical methods used for workflow analysis, such as ethnographic observations and interviewing, are limited in their ability to capture activities from different perspectives simultaneously. This limitation, coupled with the complexity and dynamic nature of clinical environments makes understanding the nuances of clinical workflow difficult. The methods proposed in this research aim to provide a quantitative means of capturing and analyzing workflow. The approach taken utilizes recordings of motion and location of clinical teams that are gathered using radio identification tags and observations. This data is used to model activities in critical care environments. The detected activities can then be replayed in 3D virtual reality environments for further analysis and training. Using this approach, the proposed system augments existing methods of workflow analysis, allowing for capture of workflow in complex and dynamic environments. The system was tested with a set of 15 simulated clinical activities that when combined represent workflow in trauma units. A mean recognition rate of 87.5% was obtained in automatically recognizing the activities.

Documenting motion sequences with a personalized annotation system

We present a novel technique for motion annotation that adapts to a persons style and vocabulary of basic movements (gestures). The system segments continuous motion sequences into gestures, which it then documents in a personalized annotation with an intuitive hierarchical representation. Initial testing suggests that software based on this technique could be an effective teaching aid for dance and sports.

Measuring movement expertise in surgical tasks

Surgical movement is composed of discrete gestures that are combined to perform complex surgical procedures. A promising approach to objective surgical skill evaluation systems is kinematics and kinetic analysis of hand movement that yields a gesture level analysis of proficiency of a performed movement. In this paper, we propose a novel system that combines surgical gesture segmentation, surgical gesture recognition, and expertise analysis of surgical profiles in minimally invasive surgery (MIS). Kinematic analysis was used to segment gestures from a continuous motion stream. Human anatomy driven Hidden Markov Models (HMMs) are adopted for gesture recognition and expertise identification. When the proposed system was tested on a library of 200 samples for every basic surgical gesture, the gesture recognition module reported a perfect accuracy rate for the basic gestures, while the expertise identification module showed 94.7% accuracy.

Effects of duty hours and time of day on surgery resident proficiency.

BACKGROUND Night floats have evolved in the era of limited resident work hours. This study was designed to define the effect of restricted nighttime duty hours on the psychomotor and cognitive skills of surgery residents. METHODS To quantify the effect of fatigue on the skills of residents on day-shift and night-float rotations, residents were asked to complete visuohaptic simulations before and after 12-hour duty periods and to rate their fatigue level with questionnaires. RESULTS Both groups showed significant decrements in proficiency measures after their shifts compared with baseline. The night-float group showed more significant declines (P < .05) in all areas assessed than the day-shift group. The night-float group was significantly less proficient in cognitive tasks after their shifts compared with the day-shift group. CONCLUSIONS The deterioration of surgical proficiency is to a degree dependent on the time of day during which call occurs, not solely on the length of call.