We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Automated Tool for Spinal Anesthesia Produces High Success Rate

Automated Tool for Spinal Anesthesia Produces High Success Rate content piece image
USINE(TM) in action. Credit: KK Women's and Children's Hospital
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 2 minutes

A study conducted by KK Women's and Children's Hospital (KKH) and National University of Singapore's (NUS) Faculty of Engineering shows that the world's first novel artificial intelligence (AI)-powered ultrasound guided automated spinal landmark identification system, called uSINETM, enhances patient care by improving the accuracy and success rate of first attempt needle insertion during spinal anaesthesia.

Led by KKH, in collaboration with researchers from the NUS Department of Electrical and Computer Engineering, uSINETM is a novel technology that uses ultrasound imaging and AI to automatically identify the spinal level of insertion and the midline, so that the spinal needle insertion involved during spinal anaesthesia can be more precise and require fewer attempts. Using a proprietary machine learning algorithm, uSINETM automatically identifies anatomical landmarks during an ultrasound scan, and the anaesthetist is alerted in real-time when the right location and right angle are found.

A clinical study led by KKH was conducted to evaluate the first attempt success rate of spinal anaesthesia using landmarks obtained from uSINETM. It involved 100 women who underwent spinal anaesthesia for surgical procedures at KKH from May 2016 to May 2017. With the use of uSINETM, the success rate for the first attempt needle insertion during spinal anaesthesia was high, at 92 per cent.

Neuraxial anaesthesia procedures are performed by placing a needle between the vertebrae and injecting medication into the epidural or spinal space. These are performed for surgical procedures such as caesarean sections, labour epidural analgesia and some gynaecological surgeries. In the United States of America, more than 1.4 million caesarean sections and 700,000 epidural procedures are performed every year.

Precise needle insertion will improve quality of anaesthesia and reduce complications such as abnormal sensation to the skin such as tingling or prickling (paraesthesia) due to nerve irritation, and blood collection within the tissues in the spine (spinal haematoma).

Spinal anaesthesia involves delivering local anaesthetics into the fluid space surrounding the spinal canal. Conventionally, a doctor uses his hands to manually identify the landmark for spinal needle insertion. This requires good knowledge of the anatomy and skills due to its complexity, and it becomes more challenging in patients who are obese, have an abnormal spine or had a previous spine surgery. Difficult needle placement could lead to higher rate of complications such as multiple attempts, failed anaesthesia, or neurological injury.

Senior author of the published study, Associate Professor Sng Ban Leong, Head and Senior Consultant, Department of Women's Anaesthesia, KKH, said, "Worldwide, we know that neuraxial anaesthesia is used in many obstetrical and gynaecological procedures. Hence as the largest maternity hospital in Singapore, we want to improve the care for our patients in this area, and uSINETM will help enable this. uSINETM not only reduces the anxiety, discomfort and pain associated with multiple needle insertions, it also reduces the complications associated with it. Furthermore, this novel AI-powered system also plays a significant role in training doctors specialising in anaesthesia to better identify correct spinal landmarks."

The study team will be looking at further enhancing the AI-powered system and investigating the use of the system in high-risk patients.

uSINETM has been licensed to NUS spin-off company, HiCura Medical Pte Ltd, for commercialisation. Efforts to introduce the AI-powered system into clinical practice are ongoing.

Reference: Oh, T.T., Ikhsan, M., Tan, K.K. et al. A novel approach to neuraxial anesthesia: application of an automated ultrasound spinal landmark identification. BMC Anesthesiol 19, 57 (2019) doi:10.1186/s12871-019-0726-6

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.