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An Update of In Vivo Application of Artificial Intelligence and Robotics for Percutaneous Nephrolithotripsy: Results from a Systematic Review

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Abstract

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.

Summary

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.

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Data Availability

Data will be provided by the corresponding author upon a reasonable request.

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Author information

Authors and Affiliations

  1. Department of Urology, Ng Teng Fong General Hospital, Singapore, Singapore

    Vineet Gauhar, Virgilio De Stefano, Giulio Milanese, Andrea Benedetto Galosi & Daniele Castellani

  2. Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Via Conca 71, 60127, Ancona, Italy

    Carlo Giulioni

  3. Department of Urology, Saint-Petersburg State University Hospital, Saint-Petersburg, Russia

    Nariman Gadzhiev

  4. S.H. Ho Urology Centre, Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China

    Jeremy Yuen-Chun Teoh

  5. Department of Urology, National University Hospital, Singapore, Singapore

    Ho Yee Tiong

  6. Department of Nephro-Urology, Nagoya City University, Nagoya, Japan

    Kazumi Taguchi

  7. Urology Unit, ASUR 5, Ascoli Piceno, Italy

    Giulio Milanese

  8. Department of Urology, University Hospitals Southampton NHS Trust, Southampton, UK

    Bhaskar Kumar Somani

Authors
  1. Vineet Gauhar
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Contributions

Vineet Gauhar and Daniele Castellani: conceptualization; writing — original draft; writing — review and editing; data curation; methodology. Carlo Giulioni, Virgilio De Stefano, Jeremy Yuen-Chun Teoh, Ho Yee Tiong, Nariman Gadzhiev, and Kazumi Taguchi: data curation; writing — original draft; figure production. Giulio Milanese, Andrea Benedetto Galosi, and Bhaskar Kuman Somani: supervision; writing — review and editing. All authors participated in manuscript writing, review, and approval of the final version of the manuscript for submission.

Corresponding author

Correspondence to Daniele Castellani.

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Kazumi Taguchi is Chief Executive Officer at NDR Medical Technology Japan. The remaining authors declare that they have no conflict of interest.

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Gauhar, V., Giulioni, C., Gadzhiev, N. et al. An Update of In Vivo Application of Artificial Intelligence and Robotics for Percutaneous Nephrolithotripsy: Results from a Systematic Review. Curr Urol Rep 24, 271–280 (2023). https://doi.org/10.1007/s11934-023-01155-8

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