Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis

• Published in: Urolithiasis Vol. 52; no. 1; p. 145
• Main Authors: Zhu, Quanjing, Cheong-Iao Pang, Patrick, Chen, Canhui, Zheng, Qingyuan, Zhang, Chongwei, Li, Jiaxuan, Guo, Jielong, Mao, Chao, He, Yong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 14.10.2024; Springer Nature B.V
• Subjects: Adult; Deep Learning; Female; Humans; Kidney Calculi - blood; Kidney Calculi - chemistry; Kidney Calculi - urine; Kidney stones; Male; Medical Biochemistry; Medicine; Medicine & Public Health; Middle Aged; Nephrology; Predictive Value of Tests; Retrospective Studies; Urinalysis - methods; Urine; Urology; Identification; Urine and blood analysis; Long short-term memory (LSTM); Kidney stone
• ISSN: 2194-7236; 2194-7228; 2194-7236
• 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.