Comprehensive analysis of artificial intelligence techniques for gynaecological cancer: symptoms identification, prognosis and prediction

Abstract

AbstractGynaecological cancers encompass a spectrum of malignancies affecting the female reproductive system, comprising the cervix, uterus, ovaries, vulva, vagina, and fallopian tubes. The significant health threat posed by these cancers worldwide highlight the crucial need for techniques for early detection and prediction of gynaecological cancers. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published from 2013 up to 2023 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and encompass AI technique for the early detection and prediction of gynaecological cancers. Based on the study of different articles on gynaecological cancer, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score. This work highlights the impact of gynaecological cancer on women belonging to different age groups and regions of the world. A detailed categorization of the traditional techniques like physical-radiological, bio-physical and bio-chemical used to detect gynaecological cancer by health organizations is also presented in the study. Besides, this work also explores the methodology used by different researchers in which AI plays a crucial role in identifying cancer symptoms at earlier stages. The paper also investigates the pivotal study years, highlighting the periods when the highest number of research articles on gynaecological cancer are published. The challenges faced by researchers while performing AI-based research on gynaecological cancers are also highlighted in this work. The features and representations such as Magnetic Resonance Imaging (MRI), ultrasound, pap smear, pathological, etc., which proficient the AI algorithms in early detection of gynaecological cancer are also explored. This comprehensive review contributes to the understanding of the role of AI in improving the detection and prognosis of gynaecological cancers, and provides insights for future research directions and clinical applications. AI has the potential to substantially reduce mortality rates linked to gynaecological cancer in the future by enabling earlier identification, individualised risk assessment, and improved treatment techniques. This would ultimately improve patient outcomes and raise the standard of healthcare for all individuals.