Department of Microbiology, University of Alabama Birmingham, Birmingham, AL, United States of America.
School of Medicine, University of Utah, Salt Lake City, UT, United States of America.
PLoS One. 2024 May 2;19(5):e0301812. doi: 10.1371/journal.pone.0301812. eCollection 2024.
Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.
肾结石是矿物质盐在尿路中结晶形成的。虽然大多数结石会随尿液排出体外，但有些结石可能会阻塞输尿管肾盂连接部或输尿管，导致严重的腰痛、血尿、呕吐和尿痛。X 射线或输尿管镜检查（URS）等成像技术通常用于检测肾结石。随后，使用体外冲击波碎石术（SWL）或激光 URS 将这些结石碎裂成更小的碎片。这两种治疗方法都会产生略有不同的患者结局。为了预测结石清除和并发症的结果，使用人工神经网络模型对 15126 例 SWL 和 2116 例 URS 患者的记录进行了训练。这些记录包括患者的体重指数和年龄等指标，以及使用各种医疗仪器和医疗保健专业人员获得的治疗结果。由于数据中结局失败的数量较少（例如，治疗并发症），实施了最近邻和合成少数过采样技术（SMOTE）模型来提高预测准确性。为了减少预测中的噪声，采用了集成建模。基于混淆矩阵的 SWL 结石清除和治疗并发症的平均预测准确率分别为 84.8%和 95.0%，而 URS 的平均预测准确率分别为 89.0%和 92.2%。基于曲线下面积的 SWL 平均预测准确率分别为 74.7%和 62.9%，而 URS 的平均预测准确率分别为 77.2%和 78.9%。总的来说，无论治疗方法或结局如何，该方法都产生了中等至高度准确的预测。这些模型被纳入了一个结石决策引擎网络应用程序（http://peteranoble.com/webapps.html），该应用程序根据患者的个体指标向医疗保健提供者建议最佳的干预措施。
