Pd42-02 percutaneous Nephrostomy vs Ureteral Stent for Hydronephrosis Secondary to Ureteric Calculi: Impact on Spontaneous Stone Passage and Health-Related Quality of Life – A Prospective Study

Morais*, Nuno; Pereira, Jose Pedro; Mota, Paulo; Dias, Emanuel; Torres, João Paulo Machado; Cordeiro, Agostinho; Anacleto, Sara; Oliveira, Carlos Augusto Fernandes de; Lima, Estêvão

doi:10.1097/01.ju.0000556558.69964.ab

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“…Over half a century, the prevalence of renal stones has exhibited a noteworthy increase: from merely 3.8% in the late 1970s, to the 8.8% in the late 2000s, and has arrived up to 11.0% in recent years [1,2]. In renal stone surgeries, urinary diversion and entry of surgical tools are necessary for urolithiasis treatment procedures, including urine drainage, stone breaking, removing procedure (percutaneous nephrolithotomy, PCNL), and other therapeutic intervention [3]. Nevertheless, conventional access through the ureter is unavailable in cases of renal stone blockage or urinary tract infection.…”

Section: Introductionmentioning

confidence: 99%

Percutaneous Nephrostomy Guidance by a Convolutional Neural Network Based Endoscopic Optical Coherence Tomography System

Wang,

Calle,

Yan

et al. 2024

Preprint

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Percutaneous nephrostomy (PCN) is a commonly used procedure for kidney surgeries. However, difficulties persist in precisely locating the PCN needle tip during its insertion into the kidney. Challenges for PCN needle guidance exist in two aspects: 1) Accurate tissue recognition, and 2) Renal blood vessel detection. In this study, we demonstrated an endoscopic optical coherence tomography (OCT) system for PCN needle guidance. Human kidney samples are utilized in the experiments. Different renal tissues including: 1) cortex, 2) medulla, 3) calyx, 4) fat, and 5) pelvis can be clearly distinguished based on their OCT imaging features. We conduct kidney perfusion experiments to mimic the renal blood flow. Our system can efficiently detect the blood flow in front of PCN needle using Doppler OCT function. To improve surgical guidance efficiency and alleviate the workload of radiologists, we employ convolutional neural network (CNN) methods to automate the procedure. Three CNN models including ResNet50, InceptionV3, and Xception were applied for tissue classification. All of them demonstrate promising prediction results, with InceptionV3 achieving the highest recognition accuracy of 99.6%. For automatic blood vessel detection, nnU-net was applied, and it exhibited intersection over unions (IoU) values of 0.8917 for blood vessel and 0.9916 for background.

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Section: Introductionmentioning

confidence: 99%