Purpose of review: Kidney puncture is a key step in percutaneous nephrolithotomy (PCNL). Ultrasound/fluoroscopic-guided access to the collecting systems is commonly used in PCNL. Performing a puncture is often challenging in kidneys with congenital malformations or complex staghorn stones. We aim to perform a systematic review to examine data on in vivo applications, outcomes, and limitations of using artificial intelligence and robotics for access in PCNL. Recent findings: The literature search was performed on November 2, 2022, using Embase, PubMed, and Google Scholar. Twelve studies were included. 3D in PCNL is useful for image reconstruction but also in 3D printing with definite benefits seen in improving anatomical spatial understanding for preoperative and intraoperative planning. 3D model printing and virtual and mixed reality allow for an enhanced training experience and easier access which seems to translate into a shorter learning curve and better stone-free rate compared to standard puncture. Robotic access improves the accuracy of the puncture for ultrasound- and fluoroscopic-guided access in both supine and prone positions. The potential advantage robotics are using artificial intelligence to do remote access, reduced number of needle punctures, and less radiation exposure during renal access. Artificial intelligence, virtual and mixed reality, and robotics may play a key role in improving PCNL surgery by enhancing all aspects of a successful intervention from entry to exit. There is a gradual adoption of this newer technology into clinical practice but is yet limited to centers with access and the ability to afford this.
An update of in-vivo application of artificial intelligence and robotics for percutaneous nephrolithotripsy. Results from a systematic review / Gauhar, Vineet; Giulioni, Carlo; Gadzhiev, Nariman; DE STEFANO, Virgilio; Yuen-Chun Teoh, Jeremy; Yee Tiong, Ho; Taguchi, Kazumi; Milanese, Giulio; Galosi, Andrea Benedetto; Kumar Somani, Bhaskar; Castellani, Daniele. - In: CURRENT UROLOGY REPORTS. - ISSN 1527-2737. - (2023). [10.1007/s11934-023-01155-8]