Artificial intelligence strengthens cervical cancer screening - present and future

Abstract

Cervical cancer is a severe threat to women's health. The majority of cervical cancer cases occur in developing countries. The WHO has proposed screening 70% of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical cancer. Due to an inadequate health infrastructure and organized screening strategy, most low- and middle-income countries are still far from achieving this goal. As part of the efforts to increase performance of cervical cancer screening, it is necessary to investigate the most accurate, efficient, and effective methods and strategies. Artificial intelligence (AI) is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical images. AI will soon have a more significant role in improving the implementation of cervical cancer screening, management, and follow-up. This review aims to report the state of AI with respect to cervical cancer screening. We discuss the primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases, as well as the challenges that we anticipate in the future.

Keywords: Cervical cancer screening; artificial intelligence; deep learning algorithms.

Figure 1

Schematic representation of AI-assisted cervical cytology image analysis. (A) Whole slide image (WSI) level: Digitalization of cervical liquid-based preparation samples; (B) Patch level: WSIs are divided into smaller patches to create feature maps, focusing on significant cellular structures and detects regions of interest (ROIs); (C) Cell segmentation: Segmentation isolates nuclei from each cell, emphasizing morphologic features; (D) Cell classification: The extracted features classify cells into categories, such as LSIL, HSIL, ASC-H, and ASCUS; (E) WSI diagnosis: The classification results are aggregated to provide an overall diagnosis at the WSI level.

Figure 2

Illustrative example of an AI-assisted colposcopy-guided biopsies for a case diagnosed as HSIL/CIN2.

Figure 3

Sample interface of an AI-guided colposcopy training platform. The platform offers a structured learning, using AI to tailor content and enhance colposcopy training. (A) Displays the user learning progress and access to textbook chapters and consolidation practice exercises. (B) Provides personalized learning materials based on self-assessment results through a recommendation system. (C) Provides exercises for beginner, intermediate, and advanced levels. Includes access to guidelines, official textbooks, and terminology resources.