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Artificial intelligence in glomerular diseases.

Francesco P Schena1, Riccardo Magistroni2, Fedelucio Narducci3

  • 1Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy. paolo.schena@uniba.it.

Pediatric Nephrology (Berlin, Germany)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in analyzing patient history, kidney biopsies, and imaging for predicting chronic kidney disease (CKD). However, AI tools require further validation and multiracial data before widespread clinical adoption.

Keywords:
Artificial IntelligenceClinical OutcomeDeep Learning AlgorithmGlomerulonephritisMachine Learning AlgorithmNatural Language Processing

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Area of Science:

  • Nephrology
  • Artificial Intelligence
  • Digital Pathology

Background:

  • Artificial intelligence (AI) is increasingly explored for applications in nephrology.
  • Current AI tools face limitations in clinical validation and generalizability.

Purpose of the Study:

  • To review the current applications of AI in glomerular disease, kidney biopsy analysis, renal ultrasonography, and chronic kidney disease (CKD) prediction.
  • To highlight the potential and challenges of AI in clinical nephrology.

Main Methods:

  • Narrative review of AI applications in clinical history (natural language processing), digital pathology (deep learning algorithms), renal ultrasonography, and CKD prediction (machine learning algorithms).

Main Results:

  • AI can identify computable phenotypes from clinical histories and enhance lesion prediction in digital pathology, but requires validation.
  • Machine learning algorithms show promise for early CKD detection and support some clinical tools for glomerulonephritis, but have limitations regarding cohort diversity and data type.
  • AI tools currently require validation in prospective, multiracial cohorts before clinical integration.

Conclusions:

  • AI holds significant potential to advance nephrology diagnostics and prognostics.
  • Further research focusing on robust validation, diverse datasets, and prospective studies is crucial for integrating AI into routine clinical practice.