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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Reliable classification of polyps based on artificial intelligence: a development and validation study.

Frida M I Julbø1, Audun L Henriksen1, Manohar Pradhan1

  • 1Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway.

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Summary
This summary is machine-generated.

An AI tool, POLARIS, accurately identifies high-risk colorectal polyps, potentially reducing pathologist workload. It correctly classifies 98.94% of high-grade dysplasia and adenocarcinoma cases, aiding timely cancer diagnosis.

Keywords:
Artificial intelligenceColorectal cancerDigital pathologyPolyp classificationPrescreeningWhole-slide images

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Digital pathology and machine learning

Background:

  • Pathologist shortages create diagnostic bottlenecks.
  • An increasing volume of colorectal biopsies requires efficient diagnostic tools.
  • Artificial intelligence offers a solution for prescreening colorectal biopsies.

Purpose of the Study:

  • To develop and validate an AI-based prescreening tool, POLARIS, for colorectal biopsies.
  • To assist pathologists in managing the growing caseload of colorectal biopsies.
  • To improve the accuracy and efficiency of colorectal polyp diagnosis.

Main Methods:

  • Developed POLARIS using a foundation model (H-optimus-0) and multiple instance learning on 15,079 whole-slide images (WSIs).
  • Trained and validated the model on datasets from the UK bowel cancer screening program and Cheltenham General Hospital.
  • Classified WSIs into risk categories and validated performance using geographically external datasets and expert pathologist review.

Main Results:

  • POLARIS achieved 98.94% accuracy in identifying high-grade dysplasia (HGD) and adenocarcinoma, and 83.04% accuracy in classifying normal/low-grade dysplasia (LGD) cases.
  • The model demonstrated a balanced accuracy of 86.65% in external validation and an AUROC of 0.9449 for distinguishing LGD from polyps needing review.
  • Expert pathologists agreed with POLARIS in 92.5% of challenging cases, and AI-generated heatmaps correlated with pathologist-identified high-risk areas.

Conclusions:

  • POLARIS effectively prescreens colorectal biopsies, identifying high-risk lesions with high accuracy.
  • The AI tool can significantly reduce the number of slides requiring pathologist review, enhancing workflow efficiency.
  • POLARIS shows potential to improve diagnostic turnaround times and support pathologists in managing large caseloads.