The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

Meng Chun Kao, Chien Kun Ting, Wen Chuan Kuo*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Original languageEnglish
Title of host publicationAdvanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVI
EditorsTuan Vo-Dinh, Warren S. Grundfest, Anita Mahadevan-Jansen
PublisherSPIE
ISBN (Electronic)9781510614536
DOIs
StatePublished - 2018
EventAdvanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVI 2018 - San Francisco, United States
Duration: 28 Jan 201830 Jan 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10484
ISSN (Print)1605-7422

Conference

ConferenceAdvanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVI 2018
Country/TerritoryUnited States
CitySan Francisco
Period28/01/1830/01/18

Keywords

  • Automatic identification
  • Epidural Anesthesia
  • Optical coherence tomography (OCT)

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