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Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

BACKGROUND: Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses. CONTENT: Through an extens...

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Published in:Clinical Chemistry Vol. 69; no. 12; pp. 1348 - 1361
Main Authors: De Bruyne, Sander, De Kesel, Pieter, Oyaert, Matthijs
Format: Journal Article
Published: Oxford University Press / USA Dec2023
Online Access:View this record in EBSCOhost
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      pub: Oxford University Press / USA
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        atl: Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?
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          De Bruyne, Sander
          De Kesel, Pieter
          Oyaert, Matthijs
        affil: Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
      sug:
      ab: BACKGROUND: Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses. CONTENT: Through an extensive examination of relevant literature, this narrative review illustrates the significance and applicability of AI models across the diverse application area of urinalysis. It encompasses automated urine test strip and sediment analysis, urinary tract infection screening, and the interpretation of complex biochemical signatures in urine, including the utilization of cutting-edge techniques such as mass spectrometry and molecular-based profiles. SUMMARY: Retrospective studies consistently demonstrate good performance of AI models in urinalysis, showcasing their potential to revolutionize clinical practice. However, to comprehensively evaluate the real clinical value and efficacy of AI models, large-scale prospective studies are essential. Such studies hold the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize medical treatment strategies. By bridging the gap between research and clinical implementation, AI can reshape the landscape of urinalysis, paving the way for more personalized and effective patient care.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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