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Cervical cancer prediction with Artificial Intelligence to improve the accuracy and efficiency of early detection and diagnosis of cervical cancer.
Cervical cancer, predominantly caused by persistent infection with high-risk human papillomavirus (HR-HPV), ranks as one of the most prevalent cancers afflicting women worldwide and is a notable cause of mortality (1)(2). In 2020 alone, there were approximately 604,000 new cases and 342,000 deaths attributed to this disease (3). Around 30% of high-grade cervical intraepithelial neoplasia (CIN) lesions, precursors to cervical cancer, have the potential to develop into invasive cancer over three decades (2). The low HPV vaccine coverage further emphasizes the crucial need for consistent cervical cancer screening programs. Early detection and treatment of precursor lesions are vital in halting the progression to invasive cancer, underscoring the importance of accessible and effective public health interventions for the prevention of cervical cancer (4)(5).
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“CerviScan AI,” an artificial intelligence-based predictive tool, has been designed to streamline cervical cancer risk assessment in clinical settings. Using synthetic data, this AI employs machine learning algorithms to evaluate a series of demographic and clinical variables with the goal of detecting precancerous conditions early and guiding appropriate therapeutic measures.
The model incorporates variables including patient age, sexual history, tobacco use, contraceptive methods, and sexually transmitted diseases (STDs) derived from clinical studies. This information is synthesized into a database that reflects real-world clinical data, guided by the research of Song et al. (1), Ouh et al. (2), Egemen et al. (3), and insights into the role of AI in cervical cancer detection as reported by the National Cancer Institute (4) and Hou et al. (5).
Cervical cancer, predominantly caused by persistent infection with high-risk human papillomavirus (HR-HPV), ranks as one of the most prevalent cancers afflicting women worldwide and is a notable cause of mortality (1)(2). In 2020 alone, there were approximately 604,000 new cases and 342,000 deaths attributed to this disease (3). Around 30% of high-grade cervical intraepithelial neoplasia (CIN) lesions, precursors to cervical cancer, have the potential to develop into invasive cancer over three decades (2). The low HPV vaccine coverage further emphasizes the crucial need for consistent cervical cancer screening programs. Early detection and treatment of precursor lesions are vital in halting the progression to invasive cancer, underscoring the importance of accessible and effective public health interventions for the prevention of cervical cancer (4)(5).
‍
“CerviScan AI,” an artificial intelligence-based predictive tool, has been designed to streamline cervical cancer risk assessment in clinical settings. Using synthetic data, this AI employs machine learning algorithms to evaluate a series of demographic and clinical variables with the goal of detecting precancerous conditions early and guiding appropriate therapeutic measures.
The model incorporates variables including patient age, sexual history, tobacco use, contraceptive methods, and sexually transmitted diseases (STDs) derived from clinical studies. This information is synthesized into a database that reflects real-world clinical data, guided by the research of Song et al. (1), Ouh et al. (2), Egemen et al. (3), and insights into the role of AI in cervical cancer detection as reported by the National Cancer Institute (4) and Hou et al. (5).
