Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.
Meng-Chun Kao, Chien-Kun Ting, and Wen-Chuan Kuo, "The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia," Proc. SPIE 10484, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVI, 104840D (Presented at SPIE BiOS: January 28, 2018; Published: 12 February 2018); https://doi.org/10.1117/12.2289750.
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