Intelligent epidural needle placement using fiber-probe optical coherence tomography in a piglet model

Abstract

Incorrect needle placement during an epidural block causes medical complications such as dural puncture or spinal cord injury. We propose a system combining an optical coherence tomography imaging probe with an automatic identification algorithm to objectively identify the epidural needle-tip position and thus reduce complications during epidural needle insertion. Eight quantitative features were extracted from each two-dimensional optical coherence tomography image during insertion of the needle tip from the skin surface to the epidural space. 847 in vivo optical coherence tomography images were obtained from three anesthetized piglets. The area under the receiver operating characteristic curve was used to quantify the discriminative ability of each feature. We found a combination of six image features-mean value of intensity, mean value with depth, entropy, mean absolute deviation, root mean square, and standard deviation-showed the highest differentiating performance with the shortest processing time. Finally, differentiation of the needle tip inside or outside the epidural space was automatically evaluated using five classifiers: k-nearest neighbor, linear discriminant analysis, quadratic discriminant analysis, linear support vector machines, and quadratic support vector machine. We adopted an 8-fold cross-validation strategy with five classifications. Quadratic support vector machine classification showed the highest sensitivity (97.5%), specificity (95%), and accuracy (96.2%) among the five classifiers. This study provides an intelligent method for objective identification of the epidural space that can increase the success rate of epidural needle insertion.