Chinese Journal of Blood Purification

Chinese Journal of Blood Purification ›› 2022, Vol. 21 ›› Issue (02): 98-102.doi: 10.3969/j.issn.1671-4091.2022.02.007

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The application performance of urine quantitative analysis system based on computer vision in the screening of albuminuria

  

  1. 1Renal Division, Department of Medicine, Peking University First Hospital, and Institute of Nephrology, Peking University, Beijing 100034, China; 2Advanced Institute of Information Technology, Peking University, Zhejiang 311215, China;  3Clinical laboratory, Civil Aviation General Hospital, Peking University, Beijing 100025, China;  4Research Units of Diagnosis and Treatment of Immune- mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China; 5National Institute of Health Data Science, Peking University, Beijing 100191, China
  • Received:2021-09-23 Revised:2021-11-02 Online:2022-02-12 Published:2022-02-17
  • Contact: Pengfei Li E-mail:pfli@aiit.org.cn

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Abstract: 【Abstract】Objectives This study aimed to evaluate the diagnostic performance of a computer visionbased urine quantitative analysis system for albuminuria screening. Methods A total of 143 participants with various levels of urinary albumin to creatinine ratio (uACR) recruited from the patients subjected to urinary analysis at Peking University First Hospital during February 2021 were included in this study. Randomly selected spot urine samples were collected from these participants and measured using both clinical laboratory method and the computer vision-based urine quantitative analysis system. With the results of clinical laboratory
method as golden criteria, the diagnostic performance of the computer vision-based urine quantitative analysis system in albuminuria screening was evaluated in terms of validity, reliability, predictive value, and area under the receiver operating curve (AUC). Results In the 143 participants, albuminuria A1, A2 and A3 accounted for 59 case (41.3%), 39 cases (27.3%) and 45 cases (31.5%), respectively. The computer visionbased urine quantitative analysis system achieved better performance in albuminuria screening in terms of validity, reliability, predictive value and AUC. When albuminuria A1 was set as negative albuminuria and albuminuria A2 and A3 were set as positive albuminuria, the urine quantitative analysis system achieved an accuracy of 88.8%, a sensitivity of 94.0% and a specificity of 81.4%; the positive and negative predictive values were 87.8% and 90.6%, respectively, with an AUC of 0.962. Conclusions The computer vision-based urine quantitative analysis system had better diagnostic performance in albuminuria screening with higher accuracy, sensitivity and AUC. Due to its convenience and low cost, the computer vision-based urine quantitative analysis system is especially suitable for the preliminary screening of chronic kidney disease in large scales of population.

Key words: Urine quantitative analysis system, Computer vision, Albuminuria, Chronic kidney disease, Screening effect analysis

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