A novel approach to neuraxial anesthesia: Application of an automated ultrasound spinal landmark identification

Ting Ting Oh, Mohammad Ikhsan, Kok Kiong Tan, Sultana Rehena, Nian Lin Reena Han, Alex Tiong Heng Sia, Ban Leong Sng

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Background: Neuraxial procedures are commonly performed for therapeutic and diagnostic indications. Currently, they are typically performed via palpation-guided surface landmark. We devised a novel intelligent image processing system that identifies spinal landmarks using ultrasound images. Our primary aim was to evaluate the first attempt success rate of spinal anesthesia using landmarks obtained from the automated spinal landmark identification technique. Methods: In this prospective cohort study, we recruited 100 patients who required spinal anesthesia for surgical procedures. The video from ultrasound scan image of the L3/4 interspinous space in the longitudinal view and the posterior complex in the transverse view were recorded. The demographic and clinical characteristics were collected and analyzed based on the success rates of the spinal insertion. Results: Success rate (95%CI) for dural puncture at first attempt was 92.0% (85.0-95.9%). Median time to detection of posterior complex was 45.0 [IQR: 21.9, 77.3] secs. There is good correlation observed between the program-recorded depth and the clinician-measured depth to the posterior complex (r = 0.94). Conclusions: The high success rate and short time taken to obtain the surface landmark with this novel automated ultrasound guided technique could be useful to clinicians to utilise ultrasound guided neuraxial techniques with confidence to identify the anatomical landmarks on the ultrasound scans. Future research would be to define the use in more complex patients during the administration of neuraxial blocks. Trial registration: This study was retrospectively registered on clinicaltrials.gov registry (NCT03535155) on 24 May 2018.

Original languageEnglish
Article number57
JournalBMC Anesthesiology
Volume19
Issue number1
DOIs
Publication statusPublished - 16 Apr 2019

Keywords

  • Automated
  • Neuraxial anesthesia
  • Spinal Anaesthetics: anatomy
  • Ultrasound

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