Scholarlink

Introduction <p>The current systematic review aims to summarize the existing data on intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy in order to assess which particular applications are feasible and have prospects of wide implementation into practice.</p> Materials and methods <p>The review included studies where adult patients with urolithiasis underwent any type of endoscopic lithotripsy with intraoperative application of AI. Preclinical trials (on animals or phantom kidneys) focusing on modelling endoscopic lithotripsy and AI application for this procedure were also considered.</p> Results <p>Six articles were included. The primary AI applications can be categorized into three domains: intraoperative navigation; tissue and stone differentiation; stone classification according to chemical composition. AI enabled reconstruction of the 3D map of endoscope movement with an accuracy of 0.6&#xa0;mm and stone size measurement with an accuracy of 0.06&#xa0;mm, differentiating between laser interactions with stone and tissue and differentiating between 4 common stone types (calcium oxalate monohydrate, calcium oxalate dihydrate, uric acid, and brushite). However, most of the data was obtained in experimental setups, rendering AI performance in real clinical settings still unclear.</p> Conclusion <p>This review found that AI technologies show promise in endoscopic lithotripsy, with current systems already capable of performing accurate tissue and stone segmentation, intraoperative navigation, and stone classification, although at the moment still there is no solid clinical background, and our conclusions are predominantly based on experimental studies. Currently AI clinical utility is still questionable due to lack of studies, especially validated ones. Continued development and clinical adoption are needed to further improve urological surgery outcomes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy: a systematic review from EAU endourology

  • 期刊论文

  • 发表日期：

    2025年10月30日

查看全文
  • Andrey Morozov,
  • Igor Matkovskiy,
  • Bhaskar Somani,
  • Jack Baniel,
  • Olivier Traxer,
  • David Lifshitz,
  • Stanislav Ali,
  • Yaron Erlich,
  • Shay Golan,
  • Dmitry Pushkar,
  • Giovanni E. Cacciamani,
  • Vineet Gauhar,
  • Dmitry Enikeev

摘要

Introduction

The current systematic review aims to summarize the existing data on intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy in order to assess which particular applications are feasible and have prospects of wide implementation into practice.

Materials and methods

The review included studies where adult patients with urolithiasis underwent any type of endoscopic lithotripsy with intraoperative application of AI. Preclinical trials (on animals or phantom kidneys) focusing on modelling endoscopic lithotripsy and AI application for this procedure were also considered.

Results

Six articles were included. The primary AI applications can be categorized into three domains: intraoperative navigation; tissue and stone differentiation; stone classification according to chemical composition. AI enabled reconstruction of the 3D map of endoscope movement with an accuracy of 0.6 mm and stone size measurement with an accuracy of 0.06 mm, differentiating between laser interactions with stone and tissue and differentiating between 4 common stone types (calcium oxalate monohydrate, calcium oxalate dihydrate, uric acid, and brushite). However, most of the data was obtained in experimental setups, rendering AI performance in real clinical settings still unclear.

Conclusion

This review found that AI technologies show promise in endoscopic lithotripsy, with current systems already capable of performing accurate tissue and stone segmentation, intraoperative navigation, and stone classification, although at the moment still there is no solid clinical background, and our conclusions are predominantly based on experimental studies. Currently AI clinical utility is still questionable due to lack of studies, especially validated ones. Continued development and clinical adoption are needed to further improve urological surgery outcomes.