Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


36869.pdf (10.76 MB)

ETD Abstract Container

Abstract Header

A Preliminary Investigation into using Artificial Neural Networks to Generate Surgical Trajectories to Enable Semi-Autonomous Surgery in Space

Korte, Christopher M.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595499765813353

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
This thesis is a preliminary investigation into using artificial neural networks to generate surgical trajectories towards space-based surgery in non-near earth environments. The first study focused on the communication-delay associated with tele-operation. This study was performed to find the threshold of the communication delay where tele-surgery would not be feasible. This is important because if humans are going to travel to Mars, semi-autonomous surgical methods must be developed as an alternative means of providing surgical intervention. We found a delay of 1.5 seconds was about the threshold where tele-operation becomes excessively difficult. An IRB study was conducted using surgeons and surgical residents who performed virtual surgical procedures in a virtual dissection simulator to acquire training data to use to train LSTM-RNNs. The data obtained from that study was reduced, because the sampling rate of the simulator was too high and the datasets contained too many data points to train the LSTM-RNNs effectively. The procedure was segmented into three subtasks. Several LSTM-RNN were trained using a custom cost function and evaluated using custom metrics. This method was compared with another algorithm, which was used to generate surgical trajectories. We tested the LSTM-RNN several shifted set of fiducial markers to assess its robustness. We found the LSTM-RNNs were robust enough to handle slight changes in anatomy.
Catharine McGhan, Ph.D. (Committee Chair)
Kelly Cohen, Ph.D. (Committee Member)
Ou Ma, Ph.D. (Committee Member)
Grant Schaffner, Ph.D. (Committee Member)
478 p.
Aerospace Materials
LSTM-RNN; semi-autonomous surgery; trajectory planning

Recommended Citations

Citations

  • Korte, C. M. (2020). A Preliminary Investigation into using Artificial Neural Networks to Generate Surgical Trajectories to Enable Semi-Autonomous Surgery in Space [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595499765813353

    APA Style (7th edition)

  • Korte, Christopher. A Preliminary Investigation into using Artificial Neural Networks to Generate Surgical Trajectories to Enable Semi-Autonomous Surgery in Space. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595499765813353.

    MLA Style (8th edition)

  • Korte, Christopher. "A Preliminary Investigation into using Artificial Neural Networks to Generate Surgical Trajectories to Enable Semi-Autonomous Surgery in Space." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595499765813353

    Chicago Manual of Style (17th edition)

ucin1595499765813353
579
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.