Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients

Jason Ju In Chan, Jun Ma, Yusong Leng, Kok Kiong Tan, Chin Wen Tan, Rehena Sultana, Alex Tiong Heng Sia, Ban Leong Sng, Jason Ju In Chan, Jun Ma, Yusong Leng, Kok Kiong Tan, Chin Wen Tan, Rehena Sultana, Alex Tiong Heng Sia, Ban Leong Sng

Abstract

Background: Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point of needle insertion to improve procedural success, in particular in obesity. We developed an ultrasound-guided automated spinal landmark identification program to assist anesthetists on spinal needle insertion point with a graphical user interface for spinal anesthesia.

Methods: Forty-eight obese patients requiring spinal anesthesia for Cesarean section were recruited in this prospective cohort study. We utilized a developed machine learning algorithm to determine the needle insertion point using automated spinal landmark ultrasound imaging of the lumbar spine identifying the L3/4 interspinous space (longitudinal view) and the posterior complex of dura mater (transverse view). The demographic and clinical characteristics were also recorded.

Results: The first attempt success rate for spinal anesthesia was 79.1% (38/48) (95%CI 65.0 - 89.5%), followed by successful second attempt of 12.5% (6/48), third attempt of 4.2% (2/48) and 4th attempt (4.2% or 2/48). The scanning duration of L3/4 interspinous space and the posterior complex were 21.0 [IQR: 17.0, 32.0] secs and 11.0 [IQR: 5.0, 22.0] secs respectively. There is good correlation between the program recorded depth of the skin to posterior complex and clinician measured depth (r = 0.915).

Conclusions: The automated spinal landmark identification program is able to provide assistance to needle insertion point identification in obese patients. There is good correlation between program recorded and clinician measured depth of the skin to posterior complex of dura mater. Future research may involve imaging algorithm improvement to assist with needle insertion guidance during neuraxial anesthesia.

Trial registration: This study was registered on clinicaltrials.gov registry ( NCT03687411 ) on 22 Aug 2018.

Keywords: Automated; Neuraxial anesthesia; Spinal; Ultrasound.

Conflict of interest statement

Ban Leong Sng is an associate editor of BMC Anesthesiology. All other authors declare that they have no competing interests.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
GUI for the longitudinal view
Fig. 2
Fig. 2
GUI for the transverse view
Fig. 3
Fig. 3
System setup of the automated ultrasound-guided spinal landmark identification program
Fig. 4
Fig. 4
Pearson’s correlation and Cronbach’s alpha between program recorded depth of the skin to posterior complex and the clinician measured depth

References

    1. Osterman MJ, Martin JA. Epidural and spinal anesthesia use during labor: 27-state reporting area, 2008. Natl Vital Stat Rep. 2011;59(5):1–13.
    1. Hermanides J, et al. Failed epidural: causes and management. Br J Anaesth. 2012;109(2):144–154. doi: 10.1093/bja/aes214.
    1. Ready LB. Acute pain: lessons learned from 25,000 patients. Reg Anesth Pain Med. 1999;24(6):499–505.
    1. Sawyer RJ, et al. Peripheral nerve injuries associated with anaesthesia. Anaesthesia. 2000;55(10):980–991. doi: 10.1046/j.1365-2044.2000.01614.x.
    1. Paech MJ, Godkin R, Webster S. Complications of obstetric epidural analgesia and anaesthesia: a prospective analysis of 10,995 cases. Int J Obstet Anesth. 1998;7(1):5–11. doi: 10.1016/S0959-289X(98)80021-6.
    1. Saravanakumar K, Rao SG, Cooper GM. Obesity and obstetric anaesthesia. Anaesthesia. 2006;61(1):36–48. doi: 10.1111/j.1365-2044.2005.04433.x.
    1. Whitty R, Moore M, Macarthur A. Identification of the lumbar interspinous spaces: palpation versus ultrasound. Anesth Analg. 2008;106(2):538–540. doi: 10.1213/ane.0b013e31816069d9.
    1. Ecimovic P, Loughrey JP. Ultrasound in obstetric anaesthesia: a review of current applications. Int J Obstet Anesth. 2010;19(3):320–326. doi: 10.1016/j.ijoa.2010.03.006.
    1. Shaikh F, et al. Ultrasound imaging for lumbar punctures and epidural catheterisations: systematic review and meta-analysis. BMJ. 2013;346:f1720. doi: 10.1136/bmj.f1720.
    1. Ultrasound guided catheterization of the epidural space: understanding NICE guidance. Interventional Procedures Guidance [IPG249] 2008. Available from: . Cited 2019 15 October.
    1. Geng J, et al. Ultrasound imaging increases first-attempt success rate of neuraxial block in elderly patients. Zhonghua Yi Xue Za Zhi. 2016;96(43):3459–3463.
    1. Margarido CB, et al. Anesthesiologists’ learning curves for ultrasound assessment of the lumbar spine. Can J Anaesth. 2010;57(2):120–126. doi: 10.1007/s12630-009-9219-2.
    1. Deacon AJ, Melhuishi NS, Terblanche NC. CUSUM method for construction of trainee spinal ultrasound learning curves following standardised teaching. Anaesth Intensive Care. 2014;42(4):480–486. doi: 10.1177/0310057X1404200409.
    1. Halpern SH, et al. The use of ultrasound for lumbar spinous process identification: a pilot study. Can J Anaesth. 2010;57(9):817–822. doi: 10.1007/s12630-010-9337-x.
    1. Kerby B, et al. Automatic identification of lumbar level with ultrasound. Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:2980–2983.
    1. Yu S, et al. Feature extraction and classification for ultrasound images of lumbar spine with support vector machine. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4659–4662.
    1. Yusong L, et al. Development of a real-time lumbar ultrasound image processing system for epidural needle entry site localization. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:4093–4096.
    1. Ikhsan M, et al. Gabor-based automatic spinal level identification in ultrasound. Annu Int Conf IEEE Eng Med Biol Soc. 2017;2017:3146–3149.
    1. Oh TT, et al. A novel approach to neuraxial anesthesia: application of an automated ultrasound spinal landmark identification. BMC Anesthesiol. 2019;19(1):57. doi: 10.1186/s12871-019-0726-6.
    1. Shaylor R, et al. High success rates using ultrasound for neuraxial block in obese patients. Isr Med Assoc J. 2016;18(1):36–39.
    1. Hood DD, Dewan DM. Anesthetic and obstetric outcome in morbidly obese parturients. Anesthesiology. 1993;79(6):1210–1218. doi: 10.1097/00000542-199312000-00011.
    1. Chin KJ, et al. Ultrasound imaging facilitates spinal anesthesia in adults with difficult surface anatomic landmarks. Anesthesiology. 2011;115(1):94–101. doi: 10.1097/ALN.0b013e31821a8ad4.
    1. Lee A. Ultrasound in obstetric anesthesia. Semin Perinatol. 2014;38(6):349–358. doi: 10.1053/j.semperi.2014.07.006.
    1. Grau T, et al. Ultrasound imaging improves learning curves in obstetric epidural anesthesia: a preliminary study. Can J Anaesth. 2003;50(10):1047–1050. doi: 10.1007/BF03018371.
    1. Vallejo MC, et al. Ultrasound decreases the failed labor epidural rate in resident trainees. Int J Obstet Anesth. 2010;19(4):373–378. doi: 10.1016/j.ijoa.2010.04.002.
    1. Grau T, et al. The lumbar epidural space in pregnancy: visualization by ultrasonography. Br J Anaesth. 2001;86(6):798–804. doi: 10.1093/bja/86.6.798.
    1. Mace HS, Paech MJ, McDonnell NJ. Obesity and obstetric anaesthesia. Anaesth Intensive Care. 2011;39(4):559–570. doi: 10.1177/0310057X1103900410.
    1. Uppot RN, et al. Impact of obesity on medical imaging and image-guided intervention. AJR Am J Roentgenol. 2007;188(2):433–440. doi: 10.2214/AJR.06.0409.

Source: PubMed

Sponsorer og samarbeidspartnere

Medisinsk tilstand

Legemiddelintervensjoner

3
Abonnere