S. Swaroop Vedula

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BACKGROUND Details about the type of analysis (e.g., intent to treat [ITT]) and definitions (i.e., criteria for including participants in the analysis) are necessary for interpreting a clinical trial's findings. Our objective was to compare the description of types of analyses and criteria for including participants in the publication (i.e., what was(More)
The growing availability of data from robotic and laparoscopic surgery has created new opportunities to investigate the modeling and assessment of surgical technical performance and skill. However, previously published methods for modeling and assessment have not proven to scale well to large and diverse data sets. In this paper, we describe a new approach(More)
BACKGROUND Previous studies have documented strategies to promote off-label use of drugs using journal publications and other means. Few studies have presented internal company communications that discussed financial reasons for manipulating the scholarly record related to off-label indications. The objective of this study was to build on previous studies(More)
Dexterous surgical activity is of interest to many researchers in human motion modeling. In this paper, we describe a dataset of surgical activities and release it for public use. The dataset was captured using the da Vinci Surgical System and consists of kinematic and video from eight surgeons with different levels of skill performing five repetitions of(More)
PURPOSE Previous work on surgical skill assessment using intraoperative tool motion has focused on highly structured surgical tasks such as cholecystectomy and used generic motion metrics such as time and number of movements. Other statistical methods such as hidden Markov models (HMM) and descriptive curve coding (DCC) have been successfully used to assess(More)
Current methods for manual evaluation of surgical skill yield a global score for the entire task. The global score does not inform surgical trainees about where in the task they need to improve. We developed and evaluated a framework to automatically generate an objective score for assessing skill in maneuvers (circumscribed segments) within a surgical(More)
We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level(More)