Accurate guidance of the epidural needle is important for ensuring the safety and efficacy of epidural anesthesia. Within this study, we proposed an endoscopic system built on polarization-sensitive optical coherence tomography (PS-OCT). To evaluate its viability, we performed experiments on ex-vivo human epidural specimens. Throughout the experimental process, we captured and analyzed various layers of spinal tissue that the epidural needle goes through during the surgery, including subcutaneous fat, supraspinous ligament, interspinous ligament, ligamentum flavum, epidural space, dura, and the spinal cord. Each of these tissue layers had distinctive OCT imaging patterns. Furthermore, we employed deep learning techniques for automated tissue recognition.
The proper guidance of the epidural needle is crucial for safe and effective epidural anesthesia. In this research, we developed an innovative endoscopic system based on polarization-sensitive optical coherence tomography (PS-OCT). To assess its feasibility, we conducted experiments using ex vivo human epidural specimens. During the experiments, we imaged and analyzed different spinal tissue layers that the epidural needle passes through, including subcutaneous fat, supraspinous and interspinous ligament, ligamentum flavum, epidural space, dura, and spinal cord. Each of these tissue layers exhibited distinct imaging patterns. In addition, we used deep learning for automatic tissue recognition.
Early and accurate detection of renal tumor malignancy remains a critical challenge in clinical cancer diagnosis and treatment. Unfortunately, a third of all patients aren’t diagnosed until they have advanced disease. Percutaneous renal biopsy (PRB) followed by histopathology is the most commonly used surgical procedure for early kidney detection and diagnosis. However, PRB is challenging in precisely recognizing the tumor tissue and avoiding renal hemorrhage. In this project, we developed an endoscopic polarization-sensitive optical coherence tomography (PS-OCT) probe for PRB guidance. Deep-learning method was used to automate the tumor recognition procedure.
Percutaneous nephrostomy (PCN) is a minimally invasive procedure used in kidney surgery. PCN needle placement is of great importance for the following successful renal surgery. In this study, we designed and built an endoscopic polarization-sensitive optical coherence tomography (PS-OCT) system for the PCN needle guidance. Compared to traditional OCT, PS-OCT will allow more accurate differentiation of the renal tissue types in front of the needle. In the experiment, we imaged different renal tissues from human kidneys using the PS-OCT endoscope. Furthermore, deep learning methods were applied for automatic recognition of different tissue types.
Guidance of epidural needle is important for the safe and efficient epidural anesthesia procedure. In this study, we built an endoscopic system based on polarization-sensitive optical coherence tomography (PS-OCT). We used pig backbones to test the feasibility of our PS-OCT endoscopic system. Different spinal tissue layers that epidural needle punctures through including subcutaneous fat, ligament, ligamentum flavum, epidural space and spinal cord were imaged and analyzed. They showed different imaging features on the PS-OCT imaging results. Furthermore, we applied deep-learning methods to classify those tissue types automatically to improve the recognition efficiency.
When the epidural needle is punctured into human body during epidural anesthesia surgery, the location of the needle tip is of great importance. In our study, we developed an OCT endoscopic system to help locate the needle tip in real time. Backbones from pigs were utilized to test our system. According to the tissue types that epidural needle punctures through, we imaged five different tissues (fat, ligament, flavum, epidural space and spinal cord). Furthermore, deep-learning methods were used to automatically distinguish the tissue types and predict the distance between the needle tip and the spinal cord. We achieved an average prediction accuracy of 96.65% in tissue classification, and an absolute percentage error at 3.05%±0.55% in distance measurement.
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