A Systematic Review and Research Recommendations on Artificial Intelligence for Automated Cervical Cancer Detection

Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)-assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time-consuming, and error-prone. The purpose of this study is to present a comprehensive review of AI technologies used for detecting cervical pre-cancerous lesions and cancer. The review study includes studies where AI was applied to Pap Smear test (cytological test), colposcopy, sociodemographic data and other risk factors, histopathological analyses, magnetic resonance imaging-, computed tomography-, and positron emission tomography-scan-based imaging modalities. We performed searches on Web of Science, Medline, Scopus, and Inspec. The preferred reporting items for systematic reviews and meta-analyses guidelines were used to search, screen, and analyze the articles. The primary search resulted in identifying 9745 articles. We followed a strict inclusion and exclusion criteria, which include search windows of the last decade, journal articles, and machine/deep learning-based methods. A total of 58 studies have been included in the review for further analysis after identification, screening, and eligibility evaluation. Our review analysis shows that deep learning models are preferred for imaging techniques, whereas machine learning-based models are preferred for sociodemographic data. The analysis shows that convolutional neural network-based features yielded representative characteristics for detecting pre-cancerous lesions and CrC. The review analysis also highlights the need for generating new and easily accessible diverse datasets to develop versatile models for CrC detection. Our review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision-making of automated CrC detection models. Our review suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta-learning should also be explored.