Department of Urology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey.
Department of Computer Engineering, Erzurum Technical University, Erzurum, Turkey.
Int Urol Nephrol. 2024 Jul;56(7):2179-2186. doi: 10.1007/s11255-024-03955-4. Epub 2024 Feb 10.
Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction.
The files of patients presenting with distal ureteral stones were retrospectively evaluated. Those who experienced spontaneous passage were assigned to Group P, while those who did not were assigned to Group N. Demographic and clinical data of both groups were compared. Then, logistic regression analysis was performed to determine the factors predicting spontaneous stone passage. Based on these factors, a logistic regression model was prepared, and artificial intelligence algorithms trained on the dataset were compared with this model to evaluate the effectiveness of artificial intelligence in predicting spontaneous stone passage.
When comparing stone characteristics and NCCT findings, it was found that the stone size was significantly smaller in Group P (4.9 ± 1.7 mm vs. 6.8 ± 1.4 mm), while the ureteral diameter was significantly higher in Group P (3.3 ± 0.9 mm vs. 3.1 ± 1.1 mm) (p < 0.05). Parameters such as stone HU, stone radiopacity, renal pelvis AP diameter, and perirenal stranding were similar between the groups. In multivariate analysis, stone size and alpha-blocker usage were significant factors in predicting spontaneous stone passage. The ROC analysis for the logistic regression model constructed from the significant variables revealed an area under the curve (AUC) of 0.835, with sensitivity of 80.1% and specificity of 68.4%. AI algorithms predicted the spontaneous stone passage up to 92% sensitivity and up to 86% specifity.
AI algorithms are high-powered alternatives that can be used in the prediction of spontaneous distal ureteral stone passage.
确定预测远端输尿管结石自行排出的相关因素，并评估人工智能在预测中的有效性。
回顾性分析就诊于我院的远端输尿管结石患者的病例资料。结石自行排出的患者归入 P 组，未自行排出的归入 N 组。比较两组患者的一般资料及临床数据，采用 Logistic 回归分析确定预测结石自行排出的相关因素，基于这些因素建立 Logistic 回归模型，并与人工智能算法进行比较，以评估人工智能在预测结石自行排出中的有效性。
对比结石特征和 NCCT 检查结果发现，P 组的结石直径明显更小（4.9±1.7mm 比 6.8±1.4mm），而 P 组的输尿管直径明显更大（3.3±0.9mm 比 3.1±1.1mm）（p<0.05）。两组的结石 CT 值、结石密度、肾盂前后径、肾周渗出等参数无显著差异。多因素分析显示，结石直径和α受体阻滞剂的使用是预测结石自行排出的独立因素。基于这些显著变量建立的 Logistic 回归模型的 ROC 分析显示曲线下面积（AUC）为 0.835，灵敏度为 80.1%，特异度为 68.4%。人工智能算法预测结石自行排出的灵敏度高达 92%，特异度高达 86%。
人工智能算法是一种预测远端输尿管结石自行排出的有效工具。
