Abstract
The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images. We describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following PRISMA-ScR. Search strategy. The Arksey and O’Malley methodology was used and searched PubMed, Scopus, and Google Scholar databases using a combination of English and Spanish keywords. Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases. Data collection and analysis. A descriptive summary organized by: 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 algorithms applied in the articles included SVM, Random Forest Classifier, KNN, MLP, C4.5, Naïve Bayes, AdaBoost, XGboots, Conditional Random Fields, Bayes Classifier, CNN (and variations), ResNet (several versions), YOLO+Efficientb0, and VGG (several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%. We conclude 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.
Presenters
Marcela ArrivillagaDirector of Research Office, Pontificia Universidad Javeriana, Valle del Cauca, Colombia
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2025 Special Focus—Emotional vs Artificial Intelligence: A Paradigm Shift in Healthcare?
KEYWORDS
Uterine Cervical Neoplasms, Cervical Cancer, Artificial Intelligence
