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

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.

Development of a real-time lumbar ultrasound image processing system for epidural needle entry site localization.

A fully-automatic ultrasound image processing system that can determine the needle entry site for epidural anesthesia (EA) in real time is presented in this paper. Neither the knowledge of anesthetists nor additional hardware is required to operate the system, which firstly directs the anesthetists to reach the desired insertion region in the longitudinal view, i.e.