Tracked ultrasonography snapshots enhance needle guidance for percutaneous renal access: a pilot study

Optically tracked needle for ultrasound guided percutaneous nephrolithotomy (PCNL) puncture: A preliminary report

Introduction Precise needle puncture of the renal collecting system is an essential step for successful percutaneous nephrolithotomy (PCNL). The use of ultrasound for puncture is receiving increased attention. Ultrasound has recognised limitations related to poor visualisation of the needle tip. We aimed to assess if an affordable open source computerized needle navigation training system, using optically tracked ultrasonography, could improve performance of simulated PCNL puncture by urological trainees, compared to conventional free hand manual sonographic puncture. Methods This study describes a PCNL navigation system which can be recreated with any standard ultrasound machine using relatively inexpensive components. The system allows the needle tip to be precisely appreciated in the ultrasound image, its trajectory planned, and the appreciation of needle tip to target calyx proximity aided by sound. Eight urology trainees participated in assessment of the PCNL training model. Alternating freehand (control) and tracked needle (experimental) punctures were performed on a phantom kidney. Total procedure and the number of reinsertions required were recorded. Results The mean time for freehand puncture was 89 seconds (range 13 - 173), while that of the optically tracked needle was 36 seconds (range 12 - 72). Thus, puncture time was significantly reduced by an average of 53 seconds (p=0.045) in the experimental arm. The mean number of needle reinsertions was 3,3 with freehand compared to 1,3 in the optically tracked puncture (p = 0.005). The mean square root error (MSRE) of the system was 1,8 mm. Conclusion This study demonstrates that affordable hardware and open source software can be used to construct an optically tracked ultrasound navigation system for PCNL training. Statistically significant reduced puncture time and number of passes required for successful puncture was demonstrated. We feel that computerised needle tracking during PCNL puncture deserves further evaluation in a training, and potentially, a clinical setting.

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

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.