Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks

Chen Wang; Yunlong Liu; Paul Calle; Xinwei Li; Ronghao Liu; Qinghao Zhang; Feng Yan; Kar-Ming Fung; Andrew K Conner; Sixia Chen; Chongle Pan; Qinggong Tang

doi:10.1002/jbio.202300330

Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks

J Biophotonics. 2024 Feb;17(2):e202300330. doi: 10.1002/jbio.202300330. Epub 2023 Oct 25.

Authors

Chen Wang¹, Yunlong Liu², Paul Calle², Xinwei Li³, Ronghao Liu⁴, Qinghao Zhang¹, Feng Yan¹, Kar-Ming Fung^{5

6}, Andrew K Conner⁷, Sixia Chen⁸, Chongle Pan², Qinggong Tang¹

Affiliations

¹ Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA.
² School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA.
³ Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham, UK.
⁴ School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China.
⁵ Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
⁶ Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
⁷ Department of Neurosurgery, University of Oklahoma College of Medicine, Oklahoma City, Oklahoma, USA.
⁸ Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.

PMID: 37833242
PMCID: PMC10922538 (available on 2025-02-01)
DOI: 10.1002/jbio.202300330

Abstract

Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization-sensitive optical coherence tomography (PS-OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS-OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross-testing accuracy of 91.53%. These results showed the improved precision by PS-OCT in guiding epidural anesthesia needle placement.

Keywords: deep-learning; endoscope; epidural anesthesia guidance; polarization-sensitive optical coherence tomography.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural

MeSH terms

Anesthesia, Epidural*
Animals
Multimodal Imaging
Neural Networks, Computer
Swine
Tomography, Optical Coherence* / methods

Abstract

Publication types

MeSH terms

Grants and funding