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

Disclosures

BMC Anesthesiol. 2021;21(246) 

In This Article

Abstract and Introduction

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.

Introduction

Neuraxial procedures are commonly performed for a wide range of therapeutic and diagnostic indications. These include neuraxial anesthesia for surgery, labour epidural analgesia, neuraxial steroid injections and diagnostic lumbar punctures.[1] However, the current method of palpation to locate the point of needle insertion is known to be associated with a significant failure rate (27 to 32%).[2,3] The administration of spinal anesthesia at an inappropriately high intervertebral level may result in permanent neurological injury. Multiple puncture attempts may increase the risk of complications such as post-dural headache, paraesthesia and spinal hematoma.[4,5] The prevalence of obesity in pregnant women is increasing, ranging from 5.5 to 38.3%.[6] Neuraxial anesthesia in obesity is anatomically more challenging due to the difficulty with palpating spinal landmarks.

Neuraxial ultrasonography has become increasingly popular for neuraxial space identification,[7–9] and has since been recommended for clinical use.[9,10] It is a safe and effective technique, with increasing use as an auxiliary over physical palpation to improve the overall success rate of neuraxial procedures and to reduce insertion attempts. Geng et al. reported a first attempt success rate of neuraxial blocks using ultrasound of 68.4%.[11] A recent meta-analysis demonstrated that ultrasound imaging could reduce the risk of failed or traumatic lumbar punctures and epidural catheterization, as well as the number of insertion attempts.[9]

Neuraxial ultrasonography in obese patients is limited by the considerable scanning depth, the skill for acquiring good images and the image interpretation. The steep learning curve and difficulty of pattern recognition of spinal structures can be challenging to even experienced operators, especially when difficult spinal anatomy is present.[12–14]

We have previously developed an ultrasound-based guidance program to determine the optimal insertion site and angle for neuraxial procedures.[15–18] However, only patients with body mass index (BMI) below 30 kg/m2 were recruited. In this study, we refined the program with image processing techniques and machine learning algorithm to be used in obese patients (BMI > 30 kg/m2) undergoing spinal anesthesia.

The primary aim was to evaluate the first attempt success rate of spinal anesthesia in obese patients, using landmarks obtained from an improved automated spinal landmark identification algorithm. The primary hypothesis of the study was that the automated spinal landmark identification algorithm using an improved image processing system would achieve greater than 68.4%[11] first attempt success rate of spinal anesthesia in patients with BMI greater than 30 kg/m2.

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