• 92 per cent success rate in women with healthy weight
• 82 per cent success rate in women who are obese

Singapore, 14 June 2023 – KK Women’s and Children’s Hospital (KKH) has implemented the world’s first artificial intelligence-powered (AI) ultrasound guided automated spinal landmark identification system, to help women achieve successful spinal anaesthesia at the first attempt during childbirth.

Developed by KKH and partners, uSINE^® has achieved success rates of first-attempt spinal anaesthesia needle insertion of up to 92 per cent in healthy weight women¹, and 82 per cent in obese women². The first-attempt success rates by the conventional manual identification method in healthy weight women and obese women are about 70 per cent³ and 43 per cent⁴ respectively.

Associate Professor Sng Ban Leong, Co-founder of uSINE^®, and Head and Senior Consultant, Department of Women’s Anaesthesia, KKH, said, “A doctor typically uses his hands to manually identify the landmark for spinal needle insertion. This requires good knowledge of the anatomy, skills, and experience due to the complexity of the procedure. It becomes more challenging in patients who are obese (body mass index of over 30), have an abnormal spine or had a previous spine surgery.”

“uSINE^® leverages AI to improve the success rate of achieving precise spinal needle insertion at the first attempt. This improves the quality of anaesthesia and reduces complications such as nerve irritation, blood collection within the tissues in the spine, or in rare cases, neurological injury.”

The implementation of uSINE^® in clinical practice will benefit all women going through childbirth, particularly those who have challenging spinal anaesthesia landmarks. Obese women form the majority of this group of patients. This is of great importance, given the rising prevalence of obesity in pregnancy in Singapore which is currently at about 10 per cent⁵.

KKH is the largest maternity hospital in Singapore and performs over 3,500 spinal anaesthesia procedures each year.

Transforming delivery of obstetric anaesthesia

KKH, in collaboration with researchers from the NUS Department of Electrical and Computer Engineering, started clinical trials for uSINE^® in 2017. The novel system uses ultrasound imaging and AI to identify the spinal level of insertion and the midline, to improve the precision of the needle insertion involved during spinal anaesthesia, and minimise attempts required.

Using a proprietary machine learning algorithm, uSINE^® automatically identifies anatomical landmarks during an ultrasound scan. The anaesthetist is alerted in real-time upon identification of the right location and the right angle of insertion. The system has been licensed to HiCura Medical Pte Ltd, a medical device company, for commercialisation.

Please refer to Annex A for more details.

The clinical implementation of uSINE^® at KKH started in May 2023. There are plans to introduce the system to other healthcare institutions in Singapore, starting with those within SingHealth as early as this year. Trials are also planned to be conducted in Australia, USA and Europe.

The clinical studies are funded by the National Health Innovation Centre Innovation to Develop Grant and the National Medical Research Council Centre Grant.

¹Oh, T.T., et al. A novel approach to neuraxial anesthesia: application of an automated ultrasound spinal landmark identification. BMC Anesthesiol 19, 57 (2019). https://doi.org/10.1186/s12871-019-0726-6
²Tan, H.S., et al. Automated landmark identification for lumbar ultrasound spinal anaesthesia in obese parturients: a prospective cohort study. European Journal of Anaesthesiology (2022).
³Tulay Sahin et al. A randomized controlled trial of preinsertion ultrasound guidance for spinal anaesthesia in pregnancy: outcomes among obese and lean parturients. J Anesth 2014; 28:413-419
⁴Wang, Q., Yin, C., Wang, T. L., Ultrasound facilitates identification of combined spinal-epidural puncture in obese parturients, Chinese Medical Journal, 125, 3840-3, 2012
⁵He S, Allen JC, Razali NS, Win NM, Zhang JJ, Ng MJ, Yeo GSH, Chern BSM, Tan KH. Are women in Singapore gaining weight appropriately during pregnancy: a prospective cohort study. BMC Pregnancy Childbirth. 2019 Aug 13;19(1):290.