Benjamin Scott Simpson1,2, Michael Burns3, Robert P Dick1, Leif Saager3,4. 1. Department of Electrical Engineering and Computer ScienceUniversity of Michigan Ann Arbor MI 48109 USA. 2. L3Harris Technologies Salt Lake City UT 84116 USA. 3. Department of AnesthesiologyUniversity of Michigan Medical School Ann Arbor MI 48109 USA. 4. Department of AnesthesiologyGeorg August University of Göttingen 37075 Göttingen Germany.
Abstract
Objective: We designed, prototyped, and tested a system that measures the viscoelastic response of tissue using nondestructive mechanical probing, with the goal of aiding clinical providers during epidural needle placement. This system is meant to alert clinicians when an epidural needle is about to strike bone during insertion. Methods: During needle insertion, the system periodically mechanically stimulates and collects viscoelastic response information data from the tissue at the needle's tip using an intra-needle probe. A machine-learning algorithm detects when the needle is close to bone using the series of observed stimulations. Results: Tests run on ex vivo pig spine show that the system can reliably determine if the needle is pointed at and within 3 mm of bone. Conclusion: Our technique can successfully differentiate materials at and in front of the needle's tip. However, it does not provide the 5 mm of forewarning that we believe would be necessary for use in clinical epidural needle placement. The technique may be of use in other applications requiring tissue differentiation during needle placement or in the intended application with further technical advances. Clinical and Translational Impact Statement: This Early/Pre-Clinical Research evaluates the feasibility of a method for helping clinical providers receive feedback during epidural needle insertion-thereby reducing complication rates-without significant alterations from current workflow.
Objective: We designed, prototyped, and tested a system that measures the viscoelastic response of tissue using nondestructive mechanical probing, with the goal of aiding clinical providers during epidural needle placement. This system is meant to alert clinicians when an epidural needle is about to strike bone during insertion. Methods: During needle insertion, the system periodically mechanically stimulates and collects viscoelastic response information data from the tissue at the needle's tip using an intra-needle probe. A machine-learning algorithm detects when the needle is close to bone using the series of observed stimulations. Results: Tests run on ex vivo pig spine show that the system can reliably determine if the needle is pointed at and within 3 mm of bone. Conclusion: Our technique can successfully differentiate materials at and in front of the needle's tip. However, it does not provide the 5 mm of forewarning that we believe would be necessary for use in clinical epidural needle placement. The technique may be of use in other applications requiring tissue differentiation during needle placement or in the intended application with further technical advances. Clinical and Translational Impact Statement: This Early/Pre-Clinical Research evaluates the feasibility of a method for helping clinical providers receive feedback during epidural needle insertion-thereby reducing complication rates-without significant alterations from current workflow.