Dinesh Singh, MD, on how suction-enabled URS and AI are poised to transform stone management

Rapid advances in ureteroscope design, suction-enabled stone removal, and artificial intelligence are converging to reshape the endoscopic management of urolithiasis—offering urologists new tools to improve stone-free rates, reduce residual fragments, and anticipate surgical complexity before entering the operating room, according to Dinesh Singh, MD, an associate professor of urology, Endourology Chief, Urology; director of Laparoscopy & Endourology, Urology; and director of the Endourology Fellowship, Urology at Yale School of Medicine in New Haven, Connecticut.

For much of the field's history, ureteroscope technology evolved slowly. That has changed.

"In the last few years, there has been an explosion of new technologies available for treatment of our patients," Singh said. Digitalization of ureteroscopes has improved intraoperative visualization, while a new generation of suction-enabled devices now allows surgeons to actively evacuate stone fragments from the ureter or kidney rather than relying on passive clearance.

Suction can be delivered through 2 platforms: systems integrated into the ureteroscope itself, or through the access sheath through which the ureteroscope passes. Singh noted that both are in use at Yale.

"I am not ready to say which one has an advantage over the other in terms of ultimate efficacy and safety," he said, adding that ongoing comparative data will be needed to clarify their respective roles. The practical goal of both approaches is the same—to fragment stones into small particles and remove them thoroughly, minimizing residual burden that could serve as a future nidus for stone recurrence.

One safety consideration runs across both platforms: the potential for elevated intrarenal pressure during active suction. Singh flagged this as an area requiring ongoing attention, noting that newer scope technologies capable of measuring intrarenal pressures in real time are emerging as a potential safeguard. He also connected suction-enabled systems to a previously discussed clinical application—the management of large stones in anticoagulated patients. High-powered lasers allow ureteroscopic access to larger stone burdens in patients who cannot safely stop anticoagulation, and active suction devices are precisely the complement those cases require to achieve adequate clearance.

"This is precisely where these suction devices in attacking large stones in the kidney play a very valuable role," he said.

Looking further ahead, Singh identified artificial intelligence as a technology likely to have broad and still-undefined effects on stone care. He was candid about the limits of current prediction.

"I don't know exactly how artificial intelligence is going to affect the field," he said, "but I can speculate." Near-term applications may include more sophisticated prediction models for stone passage—incorporating a far greater number of variables than clinicians can manually weigh—as well as models to guide selection of the optimal treatment approach for a given stone burden. Singh also described a longer-range possibility: augmented reality systems that integrate patient-specific imaging to generate a preoperative surgical roadmap, allowing surgeons to plan their intrarenal navigation before the procedure begins.

"You can almost do a pregame procedure and understand how to navigate the different nooks and crannies of the kidney to get through all the stones and potentially increase stone-free rates," he said.

Both the hardware advances and the AI applications, Singh suggested, are best understood as part of a broader trajectory in which the field is rapidly accumulating tools—and is only beginning to understand how to deploy them optimally.