Narges Ahmidi

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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)
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)
In the context of minimally invasive surgery, clinical risks are highly associated with surgeons' skill in manipulating surgical tools and their knowledge of the closed anatomy. A quantitative surgical skill assessment can reduce faulty procedures and prevent some surgical risks. In this paper focusing on sinus surgery, we present two methods to identify(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)
Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve(More)
We observe that expert surgeons performing MIS learn to minimize their tool path length and avoid collisions with vital structures. We thus conjecture that an expert surgeon's tool paths can be predicted by minimizing an appropriate energy function. We hypothesize that this reference path will be closer to an expert with greater skill, as measured by an(More)
BACKGROUND Surgical tasks are performed in a sequence of steps, and technical skill evaluation includes assessing task flow efficiency. Our objective was to describe differences in task flow for expert and novice surgeons for a basic surgical task. METHODS We used a hierarchical semantic vocabulary to decompose and annotate maneuvers and gestures for 135(More)
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