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RecruitingNCT07411391SENTINEL-1Updated Apr 24, 2026

Supervised Endoscopic Tele-controlled Intelligent Lithotripsy

An interventional study of RIRS using the TaloStone T1000 RIRS platform in Renal Stone, sponsored by Chinese University of Hong Kong. Recruiting at 1 site in Hong Kong. Open to participants aged 19 Years and older. Per ClinicalTrials.gov, last updated 2026-04-24.

Sponsored by Chinese University of Hong Kong · Not applicable, Interventional, and Treatment

Phase
Not applicable
Study type
Interventional
Enrollment
15
Allocation
Not applicable
Ages
19 Years and older
Sex
All
01

Study summary

This is a phase I feasibility study to investigate the use of a novel intelligent robotic retrograde intrarenal surgery (RIRS) platform. The TaloStone T1000 RIRS system can manipulate the flexible ureteroscope, with remote control of the instruments (laser fibre or basket) and ureteral access sheath movements. Beyond teleoperation, the TaloStone T1000 RIRS system integrates AI perception models and decision-making algorithms to enable the supervised autonomous execution of critical tasks within the RIRS workflow.

Read the detailed description

I. Introduction

Retrograde intrarenal surgery (RIRS) has become a preferred method for the diagnosis and treatment of urological diseases, such as kidney stone removal. However, the complex urinary and limited visibility of existing endoscope lead to inefficient manipulation of flexible ureteroscopes. Besides, conventional flexible ureteroscopy requires repetitive manual manipulation, which often results in surgeon fatigue, mucosa injury from respiratory motion, and variable stone clearance rates, particularly in complex calyceal anatomies.

The research focuses on the development of an novel robotic system for RIRS, currently dubbed "TaloStone T1000". The robotic system platform consists of a surgeon control console, a multi-functional video cart, and patient-side robotic arm with fiber-optic-sensitized flexible ureteroscopy as shown in Fig. 1. The surgeon console with optimized design of ergonomics is equipped with haptic master devices for smooth and precise control of the robotic arm to manipulate the flexible ureteroscope as well as instruments, e.g., stone baskets and laser fibers. The system also supports seamless integration of multiple modalities, including pre-operative CT scans, intra-operative endoscopic videos, and fiber-optic sensing. Besides, the self-developed flexible ureteroscope is embedded with fiber optic sensors for real-time shape sensing, force estimation, and simultaneous intrarenal pressure control and temperature monitoring. Shape sensing enables precise navigation of the ureteroscope within the renal collecting system, and force estimation provides accurate feedback of tip contact interaction to the master devices on the surgeon control.

Moreover, AI algorithms are incorporated to assist in diagnostics and higher level of supervised surgical autonomy, thereby improving safety and efficiency. The investigators developed AI-powered diagnostics for stone sensing, laser fiber recognition, depth awareness, and CT-to-endoscopy localization. Based on the sensing results from AI-powered diagnostics, the investigators proposed a supervised framework that can automate repetitive procedures throughout in-sheath and ureter navigation, laser approaching, and laser trajectory planning. The entire operation is under supervision of the surgeon, who can use one trigger on the master device or footswitch to enable or disable the supervised automated features. The foot pedal of laser device remains to trigger laser emission by the surgeon for stone fragmentation, dusting, and pop-corning. The basic safety and essential performance of both hardware and software in the robotic system were developed under clinical standards and medical device regulations.

To date, a total of three cadaveric studies have been conducted using the robotic system. In August 2024, the investigators performed the first cadaver study of the robotic system at Prince of Wales Hospital (PWH), where user study of ergonomic manners and tele-operation control of stone treatment was investigated. The second and third cadaver studies, focusing on the AI-powered features of the robotic system, were completed at PWH in June and December 2025. Synthetic renal stones of around 3mm were retrogradely inserted to the renal collecting systems, with successful fragmentation via the robotic RIRS system using Holmium:YAG laser. Over 10 doctors from PWH and the Chinese University of Hong Kong, participated in the cadaver studies. The current system response, motion speed of the robotic system, and operations with ergonomic control console can satisfy the requirements of the doctors. In addition to the cadaver studies, the investigators have conducted a set of laboratory testing and experiments, validating its robustness and stability of the system.

Subsequent to successful cadaveric experiments, the investigators planned to further validate of the feasibility of the use of the system in clinical cases. In this study, the investigators aim to evaluate the robotic system's safety and feasibility in RIRS in a stage 1, proof of concept study that follows the concepts outlined in the IDEAL framework (Idea, Development, Exploration, Assessment, Long-term Study).

II. Methods

Aim

The aim of this study is to evaluate the feasibility and safety of performing RIRS using the TaloStone T1000 system.

Study Design

This is a prospective, single-arm study that will be conducted by investigators from The Chinese University of Hong Kong/Prince of Wales Hospital in the period from November 2025 to June 2026. The investigators are experts in endo-urological surgery and robot-assisted surgery. The study design follows the guidelines for stage 1 of the IDEAL framework. The study will be carried out in accordance with the Declaration of Helsinki of the World Medical Association and the International Conference on Harmonization - Good Clinical Practice.

The study information will be provided to subjects during a preoperative consultation by the investigators and the research staff. Subjects will be provided with approved informed consent explaining the study procedure, risks, assessments, and required compliance; and will be given ample time to make their decision regarding participation in the study.

Perioperative data and outcomes from all cases of those participating in the study will be reviewed by an independent Data and Safety Monitoring Committee (consisting two senior urologists not involved in this study) for safety and identification of serious perioperative complications (within 30 days after the surgery) as interim to safeguard study subjects. The Committee will make periodic recommendations to the study team on whether to continue, modify, or prematurely terminate the study. Any adverse events will also be immediately reported to the Clinical Research Ethics Committee of the hospital.

Reporting of this stage 1 study will follow the IDEAL Reporting Guidelines.

02

Conditions studied

  • Renal Stone

Keywords

  • Renal stone
  • Urolithiasis
  • RIRS
  • Flexible URS
  • Supervised autonomy
03

Who can participate

Ages eligible
19 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Inclusion criteria

  1. Adult patients >18 years old
  2. Renal stone(s) less than 1cm 2cm in maximal length
  3. Clinically indicated for RIRS
  4. Willingness to participate as demonstrated by giving informed consent

Exclusion criteria

Exclusion criteria

  1. Patients with no preoperative CT imaging available
  2. Patients who are not recommended to receive RIRS
  3. Severe concomitant illness that drastically shortens life expectancy or increases risk of therapeutic intervention
  4. Untreated active infection
  5. Un-corrected coagulopathy
  6. Presence of another malignancy or distant metastasis
  7. Emergency surgery
  8. Vulnerable population (e.g. mentally disabled, pregnant)
04

Study design

Phase
Not applicable
Primary purpose
Treatment
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrollment
15 participants (estimated)

Study arms

  • Experimental
    RIRS arm

    Use of the TaloStone T1000 RIRS system

    Procedure: RIRS using the TaloStone T1000 RIRS platform

Interventions

  • ProcedureRIRS using the TaloStone T1000 RIRS platform

    Retrograde intrarenal surgery (RIRS) will be performed using the TaloStone T1000 RIRS system. Beyond teleoperation, the TaloStone T1000 RIRS system integrates advanced AI perception models and decision-making algorithms to enable the autonomous execution of critical tasks within the RIRS workflow. The AI-based vision models coupled with sensors in the fURS allow real-time scene understanding, depth perception, stone size estimation, pressure and temperature feedback, and object tracking - thus establishing a robust foundation for higher level of surgical autonomy. Under supervision by the surgeon, the TaloStone T1000 RIRS system can perform supervised navigation into the collecting system, actively track a target stone, dynamically target the laser fibre tip towards a stone, plan the laser fragmentation route, and perform scope withdrawal for stone suction with re-entry.

05

What researchers measure

Primary outcomes

  1. Success rate

    Successful RIRS by the robotic system, i.e. without conversion to conventional manual RIRS

    Time frame: Intra-operative

Secondary outcomes

  1. Stone free rate

    * by non-contrast computer tomography (NCCT) performed within 1 month of surgery * without residual stone fragments, or residual stone fragments \<2mm

    Time frame: Within post-operative 1 month

  2. Operative time

    * total operative time (time from start of operation to the end of operation) * console time (from start of control of robotic system to end of control of robotic system) * robotic control time (defined as time spent manipulating the control for navigation) * robotic set-up time (from the moment the robot is pushed into the operating room to the moment the flexible ureteroscope is attached to the robot) * procedure time (total operative time - The robot set-up time) * lasing time

    Time frame: Intra-operative

  3. Total laser energy used

    Laser energy in terms of kJ

    Time frame: Intra-operative

  4. Total radiation dose during operation

    Radiation dose based on fluoroscopy readings

    Time frame: Intra-operative

  5. Surgeon radiation exposure

    \- by radiation dosimeter

    Time frame: Intra-operative

  6. Length of hospital stay

    \- days of stay as in-patient

    Time frame: During admission period (up to 30 days)

  7. Post-operative pain

    \- by visual analogue scale, from 0-10 with 10 being the most pain

    Time frame: From immediately post-operatively to discharge (day 0 to day 1)

  8. Post-operative complications

    By "Clavien-Dindo Classification"

    Time frame: Within post-operative 30 days

  9. Surgeon questionnaires

    Completed the Subjective Mental Effort Questionnaire (SMEQ) to assess subjective during RIRS surgery.

    Time frame: Immediately post-operative, day 0

  10. Surgeon questionnaires

    Completed the System Usability Scale (SUS) questionnaire to assess the subjective usability of the robotic system during RIRS surgery. The questionnaire uses a 1-5 scale, where 1 = Strongly disagree and 5 = Strongly agree.

    Time frame: Immediately post-operative, day 0

  11. Surgeon questionnaires

    Completed the NASA Task Load Index (NASA-TLX) questionnaire to assess subjective mental and physical demand during RIRS surgery. The questionnaire uses a 1-10 scale, where 1 = Very Low and 10 = Very High.

    Time frame: Immediately post-operative, day 0

  12. Surgeon questionnaires

    Completed the Simulator Sickness Questionnaire (SSQ) to assess the subjective symptoms experienced during or after RIRS surgery. The questionnaire uses a 0-3 scale, where 0 = None, 1 = Slight, 2 = Moderate, 3 = Severe

    Time frame: Immediately post-operative, day 0

  13. Surgeon questionnaires

    Completed the Likert Scales on Ergonomics and Comfort questionnaire, which assessed the subjective experience of minimal discomfort or fatigue during RIRS surgery. The questionnaire uses a 1-5 scale, where 1 = Strongly disagree and 5 = Strongly agree.

    Time frame: Immediately post-operative, day 0

06

Study locations

1 of 1 sites recruiting
  • Prince of Wales Hospital
    Hong Kong, 999077, Hong Kong
    Recruiting
07

References and documents

Publications

  • Lu Y, Chen W, Lu B, Zhou J, Chen Z, Dou Q, Liu YH. Adaptive Online Learning and Robust 3-D Shape Servoing of Continuum and Soft Robots in Unstructured Environments. Soft Robot. 2024 Apr;11(2):320-337. doi: 10.1089/soro.2022.0158. Epub 2024 Feb 6. PubMed 38324014 ↗
  • Kuntz A, Emerson M, Ertop TE, Fried I, Fu M, Hoelscher J, Rox M, Akulian J, Gillaspie EA, Lee YZ, Maldonado F, Webster RJ 3rd, Alterovitz R. Autonomous medical needle steering in vivo. Sci Robot. 2023 Sep 20;8(82):eadf7614. doi: 10.1126/scirobotics.adf7614. Epub 2023 Sep 20. PubMed 37729421 ↗
  • Wei, R., Guo, J., Lu, Y., Zhong, F., Liu, Y., Sun, D. and Dou, Q., 2024. Scale-aware monocular reconstruction via robot kinematics and visual data in neural radiance fields. Artificial Intelligence Surgery, 4(3), pp.187-198.
  • Ross T, Reinke A, Full PM, Wagner M, Kenngott H, Apitz M, Hempe H, Mindroc-Filimon D, Scholz P, Tran TN, Bruno P, Arbelaez P, Bian GB, Bodenstedt S, Bolmgren JL, Bravo-Sanchez L, Chen HB, Gonzalez C, Guo D, Halvorsen P, Heng PA, Hosgor E, Hou ZG, Isensee F, Jha D, Jiang T, Jin Y, Kirtac K, Kletz S, Leger S, Li Z, Maier-Hein KH, Ni ZL, Riegler MA, Schoeffmann K, Shi R, Speidel S, Stenzel M, Twick I, Wang G, Wang J, Wang L, Wang L, Zhang Y, Zhou YJ, Zhu L, Wiesenfarth M, Kopp-Schneider A, Muller-Stich BP, Maier-Hein L. Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge. Med Image Anal. 2021 May;70:101920. doi: 10.1016/j.media.2020.101920. Epub 2020 Nov 28. PubMed 33676097 ↗
  • Dupont PE, Degirmenci A. The grand challenges of learning medical robot autonomy. Sci Robot. 2025 Jul 30;10(104):eadz8279. doi: 10.1126/scirobotics.adz8279. Epub 2025 Jul 30. PubMed 40737383 ↗
  • Long Y, Lin A, Kwok DHC, Zhang L, Yang Z, Shi K, Song L, Fu J, Lin H, Wei W, Chen K, Chu X, Hu Y, Yip HC, Chiu PWY, Kazanzides P, Taylor RH, Liu Y, Chen Z, Wang Z, Samuel Kwok Wai Au, Dou Q. Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgery. Sci Robot. 2025 Jul 16;10(104):eadt3093. doi: 10.1126/scirobotics.adt3093. Epub 2025 Jul 16. PubMed 40668896 ↗
  • Lu, Y., Chen, W., Li, B., Lu, B., Zhou, J., Chen, Z. and Liu, Y.H., 2023. A robust graph-based framework for 3-d shape reconstruction of flexible medical instruments using multi-core fbgs. IEEE Transactions on Medical Robotics and Bionics, 5(3), pp.472-485.
  • Lu, Y., Lu, B., Li, B., Guo, H. and Liu, Y.H., 2021. Robust three-dimensional shape sensing for flexible endoscopic surgery using multi-core FBG sensors. IEEE Robotics and Automation Letters, 6(3), pp.4835-4842.
  • Chen, W., Lu, Y., Li, B., Zhou, J., Cao, H., Chen, F. and Liu, Y.H., 2024, June. Intuitive teleoperation control for flexible robotic endoscopes under unkonwn environmental interferences. In 2024 IEEE 18th International Conference on Control & Automation (ICCA) (pp. 24-29). IEEE.
  • Schlenk C, Hagmann K, Steidle F, Oliva Maza L, Kolb A, Hellings-Kuss A, Schob DS, Klodmann J, Miernik A, Albu-Schaffer A. A robotic system for solo surgery in flexible ureteroscopy: development and evaluation with clinical users. Int J Comput Assist Radiol Surg. 2023 Sep;18(9):1559-1569. doi: 10.1007/s11548-023-02883-5. Epub 2023 Apr 9. PubMed 37032384 ↗
  • Giusti G, Proietti S, Villa L, Cloutier J, Rosso M, Gadda GM, Doizi S, Suardi N, Montorsi F, Gaboardi F, Traxer O. Current Standard Technique for Modern Flexible Ureteroscopy: Tips and Tricks. Eur Urol. 2016 Jul;70(1):188-194. doi: 10.1016/j.eururo.2016.03.035. Epub 2016 Apr 14. PubMed 27086502 ↗

Study documents

  • Protocol and statistical analysis plan · Sep 5, 2025
  • Informed consent form · Aug 25, 2025
  • Informed consent form · Aug 25, 2025

Documents are hosted by the registry — open the source record to download them.

Individual participant data

Plan to share: Yes — Study protocol, statistical analysis plan, informed consent form, clinical study report, analytic code will be available.

Supporting information: Study protocol, Sap, Icf, Csr, Analytic code

08

Registry details

Key details

Study ID
NCT07411391
Lead sponsor
Chinese University of Hong Kong
Responsible party
NG Chi Fai (Professor, Chinese University of Hong Kong) — Principal investigator
First posted
Feb 13, 2026
Start date
Feb 15, 2026
Primary completion
Dec 31, 2026 (estimated)
Completion
Mar 31, 2027 (estimated)
Last update
Apr 24, 2026

Study contacts

Alex Qinyang Liu, MBBS, MSc, FRCSEd
Contact
alexliu@surgery.cuhk.edu.hk
852+35052625
Chi Fai Ng, MBChB, MD, FRCSEd
Contact
ngcf@surgery.cuhk.edu.hk
852+35052625

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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