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Application of cloud server-based machine learning for assisting pathological structure recognition in IgA

Yu-Lin Huang1, Xiao Qi Liu2, Yang Huang1

  • 1Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.

Journal of Clinical Pathology
|December 20, 2023
PubMed
Summary

Machine learning models accurately identify kidney disease regions in whole slide images. An internet-based platform facilitates widespread adoption for improved clinical decision support in IgA nephropathy diagnosis.

Keywords:
DIAGNOSISKIDNEYMachine LearningTELEPATHOLOGY

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Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Nephrology research

Background:

  • Machine learning (ML) models assist in diagnosing diseases by analyzing whole slide images (WSIs).
  • Effective ML models require robust, user-friendly, and universally applicable datasets for clinical decision support.
  • Current ML applications need to be validated on real-world clinical data.

Purpose of the Study:

  • To develop and evaluate a machine learning algorithm for localizing and classifying regions of interest (ROIs) in IgA nephropathy (IgAN) WSIs.
  • To assess the performance of an internet-based ML model for clinical decision support in nephropathology.
  • To demonstrate the efficacy of ML in segmenting pathological features within kidney tissue images.

Main Methods:

  • Whole slide images (WSIs) of primary IgA nephropathy (IgAN) were collected and annotated.
  • The H-AI-L algorithm was developed on a cloud platform for WSI viewing and ROI detection.
  • Model performance was evaluated using F1-score, precision, recall, and Matthew's correlation coefficient (MCC).

Main Results:

  • Pretrained models achieved high F1-scores for glomerular localization (0.89) and differentiation of global sclerosis (0.91).
  • The overall F1-score for multiclassification of glomerular lesions was 0.81, with a recall rate of 0.96.
  • Interstitial fibrosis/tubular atrophy lesion similarity reached 0.75, indicating good prediction accuracy.

Conclusions:

  • The ML integration algorithm effectively segments ROIs in IgAN WSIs.
  • Internet-based deployment of ML models promotes widespread adoption and utilization across multiple centers.
  • This approach supports increased analysis of WSIs for improved diagnostic capabilities.