Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks.

Abstract

Epidural anesthesia helpsmanage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization-sensitive optical coherence tomography (PS-OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS-OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross-testing accuracy of 91.53%. These results showed the improved precision by PS-OCT in guiding epidural anesthesia needle placement.