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Int J Med Robot. 2020 Mar 24. doi: 10.1002/rcs.2105. [Epub ahead of print]

Systematic Approach for Content and Construct Validation: Case Studies for Arthroscopy and Laparoscopy.

Author information

1
Department of Computer Science, University of Central Arkansas, Conway, Arkansas, USA.
2
Department of Computer Science, Florida Polytechnic University, Lakeland, Florida, USA.
3
Pulaski Academy, Little Rock, Arkansas, USA.
4
Kitware, Carrboro, North Carolina, USA.
5
Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
6
Department of Orthopedic Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

Abstract

BACKGROUND:

In minimally invasive surgery, there are several challenges for training novice surgeons, such as limited field-of-view and unintuitive hand-eye coordination due to performing the operation according to video feedback. Virtual Reality (VR) surgical simulators are a novel, risk-free, and cost-effective way to train and assess surgeons.

METHODS:

We developed VR-based simulations to accurately assess and quantify performance of two VR simulations: gentleness simulation for laparoscopy and rotator cuff repair for arthroscopy. We performed content and construct validity studies for the simulators. In our analysis, we systematically rank surgeons using data mining classification techniques.

RESULTS:

Using classification algorithms such as K-Nearest Neighbors, Support Vector Machines, and Logistic Regression we have achieved near 100% accuracy rate in identifying novices, and up to an 83% accuracy rate identifying experts. Sensitivity and specificity were up to 1.0 and 0.9, respectively.

CONCLUSION:

Developed methodology to measure and differentiate the highly ranked surgeons and less-skilled surgeons. This article is protected by copyright. All rights reserved.

KEYWORDS:

arthroscopic rotator cuff; construct validation; content validation; gentleness; minimally invasive surgery; simulator; surgeon skill measurement; virtual reality

PMID:
32207877
DOI:
10.1002/rcs.2105

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