Abstract：
Objectives: To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. Methods: A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms (\"automatic\" OR \"machine learning\" OR \"convolutional neural network\" OR \"artificial intelligence\" OR \"detection\" AND \"stone volume\"). Risk-of-bias (RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies, and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Twelve studies were selected for the final review, including three multicenter and nine single-center retrospective studies. Eleven studies completed at least 50% of the CLAIM checkpoints and only one presented a high RoB. All included studies aimed to detect kidney (5/12, 42%), ureter (2/12, 16%), or urinary (5/12, 42%) stones on noncontrast computed tomography (NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested two studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection, with sensitivity, specificity, and accuracy rates ranging from 58.7% to 100%, 68.5% to 100%, and 63% to 99.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated stone volume (r = 0.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Conclusions: AI processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stone detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urologic foreign bodies (ureteral stent and nephrostomy tubes) cases.
摘要：
目的：对人工智能（AI）性能进行系统评价以检测尿路结石。
方法：PROSPERO注册（CRD473152）对Scopus的系统搜索，WebofScience,Embase和PubMed数据库用于识别与AI结石检测或测量有关的原始研究文章。使用搜索词（\"自动\"或\"机器学习\"或\"卷积神经网络\"或\"人工智能\"或\"检测\"和\"石头体积\"）。根据CochraneRoB工具进行偏倚风险（RoB）评估，JoannaBriggs研究所非随机研究清单和医学影像人工智能清单(CLAIM).
结果：选择了12项研究进行最终审查，包括3项多中心和9项单中心回顾性研究。11项研究完成了至少50％的CLAIM检查点，只有一项研究显示了高ROB。所有纳入的研究旨在检测肾脏（5/12，42%），输尿管（2/12，16％）或尿（5/12，42％）结石非对比计算机断层扫描（NCCT），但42%的人打算自动化测量。结石与血管钙化的区别感兴趣2项研究。所有研究都使用AI机器学习网络训练和内部验证，但是一个提供了外部验证。训练有素的网络实现了石头检测的灵敏度，特异性和准确率从58、.7%到100%不等，68，0.5至100%和63至99，.95%，分别。Dice检测评分为83%至97%。注意到手动和自动石头体积之间的高度相关性(r=0,.95)。区分输尿管远端结石和静脉结石似乎是可行的。
结论：人工智能过程可以从NCCT实现自动化尿路结石检测。进一步的研究应提供尿路结石的检测以及静脉结石的区别和外部验证。包括解剖异常和泌尿系统异物（输尿管支架和肾造瘘管）病例。
方法：PROSPERO注册（CRD473152）对Scopus的系统搜索，WebofScience,Embase和PubMed数据库用于识别与AI结石检测或测量有关的原始研究文章。使用搜索词（\"自动\"或\"机器学习\"或\"卷积神经网络\"或\"人工智能\"或\"检测\"和\"石头体积\"）。根据CochraneRoB工具进行偏倚风险（RoB）评估，JoannaBriggs研究所非随机研究清单和医学影像人工智能清单(CLAIM).
结果：选择了12项研究进行最终审查，包括3项多中心和9项单中心回顾性研究。11项研究完成了至少50％的CLAIM检查点，只有一项研究显示了高ROB。所有纳入的研究旨在检测肾脏（5/12，42%），输尿管（2/12，16％）或尿（5/12，42％）结石非对比计算机断层扫描（NCCT），但42%的人打算自动化测量。结石与血管钙化的区别感兴趣2项研究。所有研究都使用AI机器学习网络训练和内部验证，但是一个提供了外部验证。训练有素的网络实现了石头检测的灵敏度，特异性和准确率从58、.7%到100%不等，68，0.5至100%和63至99，.95%，分别。Dice检测评分为83%至97%。注意到手动和自动石头体积之间的高度相关性(r=0,.95)。区分输尿管远端结石和静脉结石似乎是可行的。
结论：人工智能过程可以从NCCT实现自动化尿路结石检测。进一步的研究应提供尿路结石的检测以及静脉结石的区别和外部验证。包括解剖异常和泌尿系统异物（输尿管支架和肾造瘘管）病例。
