Dynamic Discriminant Analysis with Applications in Computational Surgery

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Dynamic Discriminant Analysis with Applications in Computational Surgery

Published Date

2017-05

Publisher

Type

Thesis or Dissertation

Abstract

Background: The field of computational surgery involves the use of new technologies to improve surgical safety and patient outcomes. Two open problems in this field include smart surgical tools for identifying tissues via backend sensing, and classifying surgical skill level using laparoscopic tool motion. Prior work in these fields has been impeded by the lack of a dynamic discriminant analysis technique capable of classifying data given systems with overwhelming similarity. Methods: Four new machine learning algorithms were developed (DLS, DPP, RELIEF-RBF, and Intent Vectors). These algorithms were then applied to the open problems within computational surgery. These algorithms are designed with the specific goal of finding regions of data with maximum discriminating information while ignoring regions of similarity or data scarcity. The results of these techniques are contrasted with current machine learning algorithms found in the literature. Results: For the tissue identification problem, results indicate that the proposed DLS algorithm provides better classification than existing methods. For the surgical skill evaluation problem, results indicate that the Intent Vectors approach provides equivalent or better classification accuracy when compared to prior art. Interpretation: The algorithms presented in this work provide a novel approach to the classification of time-series data for systems with overwhelming similarity by focusing on separability maximization while maintaining a tractable training routine and real-time classification for unseen data.

Description

University of Minnesota Ph.D. dissertation. May 2017. Major: Mechanical Engineering. Advisor: Timothy Kowalewski. 1 computer file (PDF); x, 185 pages.

Related to

Replaces

License

Collections

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Other identifiers

Suggested citation

Dockter, Rodney. (2017). Dynamic Discriminant Analysis with Applications in Computational Surgery. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/188846.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.