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

Journal: BMC anesthesiology
PMID: 34663224

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.

Authors

  • Jason Ju In Chan
    Department of Women's Anesthesia, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Yusong Leng
    Department of Electrical and Computer Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore.
  • Kok Kiong Tan
    NUS Graduate School for Sciences and Engineering, Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
  • Chin Wen Tan
    Department of Women's Anesthesia, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore.
  • Rehena Sultana
    Duke-NUS Graduate Medical School, Singapore.
  • Alex Tiong Heng Sia
    Duke-NUS Medical School, Singapore, Singapore.
  • Ban Leong Sng
    Department of Women's Anesthesia, KK Womens and Childrens Hospital, Singapore; Duke-National University of Singapore Graduate Medical School, Singapore.

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