Published May 2024 | Version Published
Journal Article Open

Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022

  • 1. ROR icon Pontificia Universidad Javeriana
  • 2. ROR icon Icesi University
  • 3. ROR icon Fundación Valle del Lili
  • 4. ROR icon University of Miami
  • 5. ROR icon California Institute of Technology

Abstract

Background

The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images.

Objectives

To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR).

Search Strategy

Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords.

Selection Criteria

Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases.

Data Collection and Analysis

A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance.

Main Results

We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%.

Conclusion

We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.

Copyright and License

Funding

This study was partially funded by the Colombian Ministry of Science, Technology, and Innovation (grant number 125189783229, 897/2021) and Pontificia Universidad Javeriana Cali, Colombia (grant number 130100131).

Contributions

Study design: Mérida Rodriguez-Lopez, Marcela Arrivillaga, Hernán Darío Vargas-Cardona, Juan P. García-Cifuentes, Paula C. Bermúdez, Andres Jaramillo-Botero. Data Acquisition: Hernán Darío Vargas-Cardona, Carlos Vergara-Sanchez. Drafted the manuscript: Hernán Darío Vargas-Cardona, Mérida Rodriguez-Lopez, Marcela Arrivillaga. All authors had substantive contributions analysis, and interpretation of the data. Likewise, all authors have critically reviewed it for important intellectual content, approved the final version to be published, and agree to be responsible for all aspects of the paper to ensure that questions regarding the accuracy or completeness of any part of the paper are properly investigated and resolved.

Supplemental Material

Table S1.: ijgo15179-sup-0001-TableS1.rtf

Files

Intl J Gynecology Obste - 2023 - Vargas‐Cardona - Artificial intelligence for cervical cancer screening Scoping review .pdf

Additional details

Funding

Pontificia Universidad Javeriana
130100131
Ministerio de Ciencia, Tecnología e Innovación
125189783229, 897/2021

Dates

Accepted
2023-09-20
Accepted
Available
2023-10-09
Version of record online
Available
2024-04-16
Issue online

Caltech Custom Metadata

Publication Status
Published