Gupta Kavita, Ricapito Anna, Lundon Dara, Khargi Raymond, Connors Chris, Yaghoubian Alan J, Gallante Blair, Atallah William M, Gupta Mantu
Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
J Endourol. 2025 Jul;39(7):738-747. doi: 10.1089/end.2024.0755. Epub 2025 Jun 2.
We sought to use artificial intelligence (AI) to develop and test calculators to predict spontaneous stone passage (SSP) using radiographical and clinical data. Consecutive patients with solitary ureteral stones ≤10 mm on CT were prospectively enrolled and managed according to American Urological Association guidelines. The first 70% of patients were placed in the "training group" and used to develop the calculators. The latter 30% were enrolled in the "testing group" to externally validate the calculators. Exclusion criteria included contraindication to trial of SSP, ureteral stent, and anatomical anomaly. Demographic, clinical, and radiographical data were obtained and fed into machine learning (ML) platforms. SSP was defined as passage of stone without intervention. Calculators were derived from data using multivariate logistic regression. Discrimination, calibration, and clinical utility/net benefit of the developed models were assessed in the validation cohort. Receiver operating characteristic curves were constructed to measure their discriminative ability. Fifty-one percent of 131 "training" patients spontaneously passed their stones. Passed stones were significantly closer to the bladder (8.6 11.8 cm, p = 0.01) and smaller in length, width, and height. Two ML calculators were developed, one supervised machine learning (SML) and the other unsupervised machine learning (USML), and compared to an existing tool Multi-centre Cohort Study Evaluating the role of Inflammatory Markers In Patients Presenting with Acute Ureteric Colic (MIMIC). The SML calculator included maximum stone width (MSW), ureteral diameter above the stone (UDA), and distance from ureterovesical junction to bottom of stone and had an area under the curve (AUC) of 0.737 upon external validation of 58 "test" patients. Parameters selected by USML included MSW, UDA, and use of an anticholinergic, and it had an AUC of 0.706. The MIMIC calculator's AUC was 0.588 (0.489-0.686). We used AI to develop calculators that outperformed an existing tool and can help providers and patients make a better-informed decision for the treatment of ureteral stones.
我们试图利用人工智能（AI）开发并测试计算器，以使用影像学和临床数据预测输尿管结石自然排出（SSP）。对CT显示为单发输尿管结石且直径≤10mm的连续患者进行前瞻性纳入，并按照美国泌尿外科学会指南进行管理。前70%的患者被纳入“训练组”，用于开发计算器。后30%的患者被纳入“测试组”，以对计算器进行外部验证。排除标准包括输尿管结石自然排出试验的禁忌症、输尿管支架置入和解剖异常。获取人口统计学、临床和影像学数据并输入机器学习（ML）平台。SSP定义为结石未经干预而排出。计算器通过多变量逻辑回归从数据中得出。在验证队列中评估所开发模型的辨别力、校准度和临床实用性/净效益。构建受试者操作特征曲线以测量其辨别能力。131例“训练”患者中有51%的患者结石自然排出。排出的结石距离膀胱明显更近（8.6±11.8cm，p = 0.01），且长度、宽度和高度更小。开发了两种ML计算器，一种是监督式机器学习（SML）计算器，另一种是无监督式机器学习（USML）计算器，并与现有的工具多中心队列研究评估炎症标志物在急性输尿管绞痛患者中的作用（MIMIC）进行比较。SML计算器纳入了结石最大宽度（MSW）、结石上方输尿管直径（UDA）以及从输尿管膀胱连接处到结石底部的距离，在对58例“测试”患者进行外部验证时，其曲线下面积（AUC）为0.737。USML选择的参数包括MSW、UDA和抗胆碱能药物的使用，其AUC为0.706。MIMIC计算器的AUC为0.588（0.489 - 0.686）。我们利用AI开发的计算器性能优于现有工具，可帮助医疗服务提供者和患者就输尿管结石的治疗做出更明智的决策。
