Department of Laboratory Medicine, West China Hospital, Sichuan University, Guoxue Lane, Wuhou District, Chengdu, 610041, China.
Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
Urolithiasis. 2024 Oct 14;52(1):145. doi: 10.1007/s00240-024-01644-6.
Kidney stones are the most common urinary system diseases, and early identification is of great significance. The purpose of this study was to use routine urine and blood detection indices to build a deep learning (DL) model to identify the presence of kidney stones in the early stage. A retrospective analysis was conducted on patients with kidney stones who were treated at West China Hospital of Sichuan University from January 2020 to June 2023. A total of 1130 individuals presenting with kidney stones and 1230 healthy subjects were enrolled. The first blood and urine laboratory data of participants at our hospital were collected, and the data were divided into a training dataset (80%) and a verification dataset (20%). Additionally, a long short-term memory (LSTM)-based adaptive feature weighting model was trained for the early identification of kidney stones, and the results were compared with those of other models. The performance of the model was evaluated by the area under the subject working characteristic curve (AUC). The important predictive factors are determined by ranking the characteristic importance of the predictive factors. A total of 17 variables were screened; among the top 4 characteristics according to the weight coefficient in this model, urine WBC, urine occult blood, qualitative urinary protein, and microcyte percentage had high predictive value for kidney stones in patients. The accuracy of the kidney stone (KS-LSTM) learning model was 89.5%, and the AUC was 0.95. Compared with other models, it has better performance. The results show that the KS-LSTM model based on routine urine and blood tests can accurately identify the presence of kidney stones. And provide valuable assistance for clinicians to identify kidney stones in the early stage.
肾结石是最常见的泌尿系统疾病，早期识别具有重要意义。本研究旨在利用常规尿液和血液检测指标构建深度学习（DL）模型，以早期识别肾结石的存在。对 2020 年 1 月至 2023 年 6 月在四川大学华西医院治疗的肾结石患者进行回顾性分析。共纳入 1130 例肾结石患者和 1230 例健康对照者。收集参与者在我院的首次血、尿实验室数据，将数据分为训练数据集（80%）和验证数据集（20%）。此外，还基于长短期记忆（LSTM）训练了自适应特征加权模型，用于早期识别肾结石，并与其他模型的结果进行了比较。通过受试者工作特征曲线（AUC）下面积评估模型的性能。通过对预测因子特征重要性进行排序来确定重要的预测因子。筛选出 17 个变量；在该模型中根据权重系数排名前 4 的特征中，尿白细胞、尿潜血、定性尿蛋白和微红细胞百分比对患者肾结石具有较高的预测价值。肾结石（KS-LSTM）学习模型的准确率为 89.5%，AUC 为 0.95。与其他模型相比，具有更好的性能。结果表明，基于常规尿液和血液检查的 KS-LSTM 模型可以准确识别肾结石的存在。并为临床医生早期识别肾结石提供有价值的帮助。
