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Artificial Intelligence-Based Cervical Cancer Screening on Images Taken during Visual Inspection with Acetic Acid: A

Roser Viñals1, Magali Jonnalagedda1,2, Patrick Petignat3

  • 1Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

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Summary

Automated algorithms show promise for cervical cancer screening using visual inspection with acetic acid (VIA). While AI tools can aid early detection, large-scale real-world testing is needed to confirm their clinical utility.

Keywords:
artificial intelligenceautomatic screeningcervical cancervisual inspection with acetic acid

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Visual inspection with acetic acid (VIA) is a WHO-recommended, low-cost method for cervical cancer screening.
  • VIA is simple but highly subjective, leading to potential diagnostic variability.
  • Automated algorithms offer a potential solution to improve objectivity and accuracy in VIA screening.

Purpose of the Study:

  • To systematically identify and analyze automated algorithms for classifying VIA images.
  • To evaluate the performance (sensitivity and specificity) of these AI-based cervical cancer screening tools.
  • To assess the potential of AI in supporting cervical cancer screening, particularly in resource-limited settings.

Main Methods:

  • Systematic literature search across PubMed, Google Scholar, and Scopus.
  • Inclusion of 11 studies meeting predefined criteria from 2608 identified articles.
  • Analysis of key features and performance metrics (sensitivity, specificity) of the highest-accuracy algorithm from each study.
  • Quality and risk of bias assessment using QUADAS-2 guidelines.

Main Results:

  • Selected algorithms demonstrated a wide range of sensitivity (0.22–0.93) and specificity (0.67–0.95).
  • Artificial intelligence algorithms show potential for objective cervical cancer screening.
  • Current studies often use small, selected datasets, limiting generalizability to real-world populations.

Conclusions:

  • AI-based algorithms have significant potential to enhance cervical cancer screening accuracy and accessibility.
  • Further large-scale validation in clinical settings is crucial for real-world feasibility assessment.
  • Integration of AI could improve screening in areas with limited healthcare infrastructure and personnel.