Deep-learning-aided forward optical coherence tomography endoscope for percutaneous nephrostomy guidance

Abstract

Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.

Computer‐aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks

“…We will continue to accelerate the models through knowledge distillation and weight pruning. Furthermore, Since the proposed OCT endoscope system can distinguish different tissue types in front of needle tip, it also has potential for guiding other needle-based interventions such as percutaneous nephrostomy (PCN) needle guidance in kidney surgery [31], epidural anesthesia imaging guidance in painless delivery [32], the tumor tissue detection in cancer diagnosis [51], and a variety of needle biopsy procedures.…”

“…where TP, true positive; TN, true negative; FP, false positive; and FN, false negative. Nested cross-validation and cross-testing were used to select an optimal model and evaluate the performance [31]. For nested cross-validation and testing, images were separated into eight folds based on the subject in which the images were taken.…”

“…The endoscopic OCT system was established on a swept‐source OCT (SS‐OCT) system which applied the laser source with 1300 nm‐center wavelength and 100 nm‐bandwidth [31]. Its schematic was shown in Figure 1.…”

“…Endoscopic OCT systems has been applied in many surgical guidance procedures such as the investigation of colon cancer [28], vitreoretinal surgery [29] and nasal tissue detection [30]. In our previous study, we developed OCT endoscope based on gradient-index (GRIN) rod lens and demonstrated its feasibility in real-time percutaneous nephrostomy (PCN) guidance [31] and epidural anesthesia guidance [32].…”

Percutaneous nephrolithotomy – the puncture

“…A step forward in this technique is to replace the optical control by an optical coherence tomography (OCT) endoscopic system. The tissue ahead of the needle was clearly distinguished as renal cortex, medulla, or calyx in animal experiments [38]. Robot-assisted puncture systems already evaluate this technique.…”