Department of Urological Sciences, University of British Columbia, Stone Centre at Vancouver General Hospital, Vancouver, BC, Canada.
Department of Urology, King Abdulaziz University, Jeddah, Saudi Arabia.
Urolithiasis. 2023 Sep 30;51(1):117. doi: 10.1007/s00240-023-01490-y.
The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify stones on computed tomography (CT) images and simultaneously classify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated CT images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy.
尿酸（UA）结石的正确诊断具有重要的临床意义，因为对于围手术期发病率高的患者，可以避免手术干预，并提供碱化治疗。我们开发并验证了一种基于机器学习（ML）的模型，用于在 CT 图像上识别结石，并同时将 UA 结石与非-UA 结石分类。这项国际多中心研究纳入了 202 例接受经皮肾镜取石术治疗肾结石的患者，这些患者的 HU 值＜800。156 例（77.2%）患者的数据用于模型开发，而来自一家跨国机构的 46 例（22.8%）患者的数据用于外部验证。共有 21,074 例肾脏和结石轮廓标注的 CT 图像使用 ResNet-18 Mask R-卷积神经网络算法进行了训练。最后，将该模型与人口统计学和临床数据串联作为结石分类的全连接层。我们的模型在检测每位患者的肾结石方面的敏感性为 100%，并且肾脏和结石轮廓的描绘在临床可接受的范围内非常精确。在开发模型中，区分 UA 与非-UA 结石的准确性为 99.9%，敏感性为 100.0%，特异性为 98.9%。在外部验证中，模型的准确性为 97.1%，敏感性为 89.4%，特异性为 98.6%。SHAP 图显示结石密度、糖尿病和尿 pH 值是分类的最重要特征。我们基于 ML 的模型能够准确地识别和描绘肾结石，并以迄今为止报道的最高预测准确性对 UA 结石与非-UA 结石进行分类。该模型可用于可靠地选择更早进行碱化治疗的候选者。
