Characterizing the Details of Spatial Construction: Cognitive Constraints and Variability.

Spatial construction-the activity of creating novel spatial arrangements or copying existing ones-is a hallmark of human spatial cognition. Spatial construction abilities predict math and other academic outcomes and are regularly used in IQ testing, but we know little about the cognitive processes that underlie them. In part, this lack of understanding is due to both the complex nature of construction tasks and the tendency to limit measurement to the overall accuracy of the end goal.

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery.

Objective: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging.

Methods: In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes.

Query-by-example surgical activity detection.

Purpose: Easy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for surgical activity segments of interest is limited by the need for manual pre-processing, which can be prohibitive at scale. We developed a content-based information retrieval method, query-by-example (QBE), to automatically detect activity segments within surgical data recordings of long duration that match a query.

Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task.

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 instances of a surgeon's knot performed by 18 surgeons.

Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment.

Objective: Task-level metrics of time and motion efficiency are valid measures of surgical technical skill. Metrics may be computed for segments (maneuvers and gestures) within a task after hierarchical task decomposition. Our objective was to compare task-level and segment (maneuver and gesture)-level metrics for surgical technical skill assessment.

String motif-based description of tool motion for detecting skill and gestures in robotic surgery.

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 for simultaneous detection of gestures and skill that can be generalized to different surgical tasks.

Data-derived models for segmentation with application to surgical assessment and training.

This paper addresses automatic skill assessment in robotic minimally invasive surgery. Hidden Markov models (HMMs) are developed for individual surgical gestures (or surgemes) that comprise a typical bench-top surgical training task. It is known that such HMMs can be used to recognize and segment surgemes in previously unseen trials.

Maximum likelihood set for estimating a probability mass function.

We propose a new method for estimating the probability mass function (pmf) of a discrete and finite random variable from a small sample. We focus on the observed counts--the number of times each value appears in the sample--and define the maximum likelihood set (MLS) as the set of pmfs that put more mass on the observed counts than on any other set of counts possible for the same sample size. We characterize the MLS in detail in this article.