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A comprehensive AI model development framework for consistent Gleason grading.

Xinmi Huo1, Kok Haur Ong1, Kah Weng Lau2,3

  • 1Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore.

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|May 9, 2024
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Summary
This summary is machine-generated.

Artificial Intelligence (AI) for Gleason grading shows promise but faces challenges. This new digital pathology workflow enhances AI accuracy and efficiency across different scanners, improving pathologist integration.

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Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background:

  • AI-based Gleason grading shows potential for pathologists.
  • Challenges include inconsistent image quality, data integration needs, and limited generalizability.
  • Scalability and adoption are hindered by these limitations.

Purpose of the Study:

  • To develop a comprehensive digital pathology workflow for AI-assisted Gleason grading.
  • To address challenges of image quality, data integration, and generalizability.
  • To improve the consistency, accuracy, and efficiency of AI in Gleason grading.

Main Methods:

  • A digital pathology workflow incorporating image quality control (A!MagQC) and cloud-based annotation (A!HistoClouds).
  • Pathologist-AI Interaction (PAI) for continuous model improvement.
  • Color augmentation and image appearance migration techniques were used to address scanner variations, with training on Akoya-scanned images and evaluation on multiple scanners.

Main Results:

  • The AI model achieved an average F1 score of 0.80 and Quadratic Weighted Kappa of 0.71 on Akoya-scanned images.
  • A generalization solution improved the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners.
  • Gleason scoring time was reduced by 43%, and PAI improved annotation efficiency by 2.5 times, enhancing model performance.

Conclusions:

  • The developed pipeline significantly advances AI-assisted Gleason grading, enhancing consistency, accuracy, and efficiency.
  • The model demonstrates outstanding performance across diverse scanners, overcoming limitations of previous scanner-specific methods.
  • This advancement facilitates seamless integration of AI tools into clinical pathology workflows.