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Automated reporting of cervical biopsies using artificial intelligence.

Mahnaz Mohammadi1, Christina Fell1, David Morrison1

  • 1School of Computer Science, University of St Andrews, St Andrews, United Kingdom.

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|April 22, 2024
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Summary
This summary is machine-generated.

Early detection of cervical cancer improves survival rates. An Artificial Intelligence (AI) algorithm for digital diagnostics achieved 93.4% malignant sensitivity in classifying cervical cancer slides, aiding pathologists.

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Area of Science:

  • Digital pathology
  • Artificial intelligence in oncology
  • Cervical cancer diagnostics

Background:

  • Early-stage invasive cervical cancer has a 5-year survival rate of 92%.
  • Awareness of cervical cancer signs and symptoms is crucial for early detection and successful treatment.
  • Pathological diagnosis of cervical biopsies is critical for accurate cancer reporting.

Purpose of the Study:

  • To develop and evaluate an Artificial Intelligence (AI) algorithm for automated reporting of digital cervical biopsies.
  • To enhance the efficiency of pathological diagnosis by identifying malignant and high-grade lesions.
  • To achieve high sensitivity for detecting malignant cervical cancer cases.

Main Methods:

  • An AI algorithm was trained and validated on 1738 cervical whole slide images (WSIs).
  • The algorithm was evaluated on an independent test set of 811 WSIs.
  • The AI's performance was assessed for classifying slides and identifying areas of interest for pathologists.

Main Results:

  • The AI algorithm achieved 93.4% malignant sensitivity in classifying slides on the independent test set.
  • The algorithm processes whole slide images (WSIs) in approximately 1.5 minutes using an NVIDIA Tesla V100 GPU.
  • The system can process various WSI formats (TIFF, iSyntax, CZI) and is extensible to others.

Conclusions:

  • The developed AI algorithm demonstrates high sensitivity for detecting malignant cervical cancer in digital biopsies.
  • This AI tool has the potential to significantly improve the efficiency and speed of pathological diagnosis.
  • Automated analysis of cervical WSIs by AI can aid pathologists in triaging cases and reducing reporting time.