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Machine Learning for Renal Pathologies: An Updated Survey.

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|July 9, 2022
PubMed
Summary

Machine learning (ML) is increasingly used in nephrology for diagnosing kidney diseases like acute kidney injury and chronic kidney disease. While ML offers faster diagnoses, a key challenge is the lack of accessible public databases for validation.

Keywords:
artificial intelligencedeep learningmachine learningrenal pathology

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Area of Science:

  • Nephrology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Renal pathologies are prevalent, causing significant morbidity and mortality.
  • Modern machine learning (ML) techniques show promise in medical applications.
  • There's a growing interest in applying ML to nephrology.

Purpose of the Study:

  • To review and analyze existing literature on ML applications in nephrology.
  • To categorize studies based on specific kidney pathologies addressed.
  • To identify trends and limitations in ML adoption in nephrology.

Main Methods:

  • A systematic literature search was conducted on a major bibliographic database up to February 2022.
  • Studies were screened based on inclusion/exclusion criteria.
  • A total of 224 studies were identified, with 59 selected for in-depth analysis.

Main Results:

  • A clear increasing trend in ML applications within nephrology was observed.
  • ML techniques are being applied to diverse renal pathologies, including renal masses, acute kidney injury, chronic kidney disease, kidney stones, glomerular disease, and kidney transplants.
  • ML is emerging as a valuable tool for physicians, aiding in more accurate and rapid diagnoses.

Conclusions:

  • Machine learning shows significant potential to enhance diagnostic capabilities in nephrology.
  • The primary limitation hindering further progress is the scarcity of publicly available datasets for model validation and collaboration.
  • Addressing the data accessibility issue is crucial for advancing ML in kidney disease research and clinical practice.