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<p>To find the cause of infectious diseases such as urinary tract infections, a the diagnostic patterning test was used. In this method, the whole blood and serum samples for every person were dried on a glass substrate to create a special pattern due to formation of central and peripheral regions. This process was performed during 1&#xa0;h at 25&#xa0;°C which is much shorter than the classical and instrumental methods. The resulting patterns can be observed by an optical microscope. The study aimed to classify the UTI patients and healthy controls (N=600). In addition, the images of dried patterns were captured by a camera and the classification analysis was done by convolutional neural network algorithm, executed by graphical user interface. The result of analysis was available after a few minutes, indicating that the proposed method achieved accuracies of 90.8% (through the analysis of the dried blood spot) and 86.6% (through the evaluation of the dried serum spot) for discriminating the patients from healthy participants. This method could become popular because it has a simple design and does not require skilled personnel, toxic reagents and expensive equipment. Most importantly, it can identify the contaminated sample as well as the contaminant in a shorter time than other common methods.</p>

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Rapid detection of urinary tract infection by analyzing the dried spots of biological fluids using convolutional neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Mohammad Mahdi Bordbar,
  • Fatemeh Nobakht M. Gh.,
  • Azarmidokht Sheini,
  • Maryam Alborz,
  • Raheleh Halabian,
  • Afsaneh Pargar,
  • Hosein Khoshsafar,
  • Gözde Baydemir Peşint,
  • Hosein Samadinia,
  • Hasan Bagheri

摘要

To find the cause of infectious diseases such as urinary tract infections, a the diagnostic patterning test was used. In this method, the whole blood and serum samples for every person were dried on a glass substrate to create a special pattern due to formation of central and peripheral regions. This process was performed during 1 h at 25 °C which is much shorter than the classical and instrumental methods. The resulting patterns can be observed by an optical microscope. The study aimed to classify the UTI patients and healthy controls (N=600). In addition, the images of dried patterns were captured by a camera and the classification analysis was done by convolutional neural network algorithm, executed by graphical user interface. The result of analysis was available after a few minutes, indicating that the proposed method achieved accuracies of 90.8% (through the analysis of the dried blood spot) and 86.6% (through the evaluation of the dried serum spot) for discriminating the patients from healthy participants. This method could become popular because it has a simple design and does not require skilled personnel, toxic reagents and expensive equipment. Most importantly, it can identify the contaminated sample as well as the contaminant in a shorter time than other common methods.