Effectiveness evaluation for a digital health intervention in proteinuria screening
Year: 2025
Type: article
Abstract: Objective: Persistent proteinuria is a hallmark of glomerulonephritis and serves as an important diagnostic and prognostic marker. This study proposes a novel digital approach––the quantitative urine protein-to-creatinine ratio (UPCR) home-testing kit (UTK)––for proteinuria screening based on advanced computer-vision algorithms, and evaluates its effectiveness. Methods: Randomly selected spot urine samples were collected from 199 participants recruited from a tertiary hospital in Beijing, China. The UTK method utilized contour detection and image segmentation algorithms to extract the topological information of the urinalysis strip for color calibration, and employed a three-dimensional color space interpolation algorithm to obtain quantitative UPCR readings. Using the laboratory results as a benchmark, we evaluated the diagnostic performance of UTK in proteinuria screening in terms of validity, reliability, predictive values, and area under the receiver operating characteristic curve (AUC). Results: The mean age of the 199 participants was 51.6 ± 16.2 years and 88 (44.2%) of them were male. The median UPCR was 222.5 mg/g (interquartile range: 106.5–844.6), and 20 (10.1%) participants were identified as having proteinuria. The UTK method performed well in proteinuria screening, with a high accuracy rate of 91.0%, 85.0% sensitivity, a 91.6% specificity, and an AUC of 0.966. The Bland-Altman plot showed a high level of agreement of the quantitative UPCR results between the UTK and laboratory methods. Conclusions: The proposed digital solution for quantitative UPCR analysis based on advanced computer-vision technologies showed good performance in proteinuria screening. This user-friendly and cost-effective UPCR measure method presents a promising new strategy for enhancing the efficiency of the primary monitoring and management of glomerulonephritis.
Authors: Jingyi Wu, Yuewen Zheng, Chunmiao Zhou, Fei Wang, Qing Li, Pengfei Li, Luxia Zhang
Institutions: Peking University, Peking University First Hospital
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409914121