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  • Artificial Intelligence for Precision Diagnosis, Prognostic Prediction, and Personalized Treatment of HPV-Associated Cervical Cancer: From Bench to Bedside

  • Amirhossein Khorramian,1,* Fatemeh Mousalou,2
    1. Department of Microbiology, Ardabil branch, Islamic Azad University, Ardabil, Iran
    2. Department of Microbiology, Ardabil branch, Islamic Azad University, Ardabil, Iran


  • Introduction: Early detection of unusual cervical cells improves the likelihood of prompt cervical cancer treatment. Because manual identification is time-consuming, prone to error, and requires skilled pathologists, automated techniques for detecting aberrant cervical cells were created. Deep learning algorithms have given several medical pictures interpretation capabilities that are comparable to those of people, and artificial intelligence (AI) is quickly increasing its usage in cancer screening and diagnosis. Artificial intelligence (AI) will quickly play a larger role in enhancing the follow-up, management, and implementation of cervical cancer screening.
  • Methods: More than 90% of cervical cancers are caused by chronic infection with high-risk human papillomavirus (HR-HPV) subtypes. By getting vaccinated against HPV at the appropriate moment, CC may be avoided. Even if markers for early diagnosis, treatment, and disease recurrence forecasting are essential for improving patient outcomes, CC cases are on the rise in LMICs. The World Health Organization's newest recommendations advise utilizing these three screening techniques for the early detection of cervical cancer: HPV testing, cytology (which includes liquid-based cytology smears and traditional Pap smears), and visual inspection with acetic acid (VIA). We chose the first two methods since VIA is only utilized when the first two methods are not accessible. Cervical cells that have been exfoliated and brushed are used as test samples in HPV testing and cytology. While a cytological examination uses a microscope to identify cells removed from the cervix for potential cervical cancer or precancerous lesions, HPV testing can identify high-risk forms of HPV infection in the cervix.
  • Results: Genotyping of HPV will make cervical cancer screening and management more straightforward since it will make it simpler to evaluate the risk for women with positive HPV DNA and positive cervical smear results. AI learning technology uses research on HPV testing to increase its precision and expand its use in cervical cancer screening.
  • Conclusion: Therefore, we sought to explore the potential applications of artificial intelligence (AI) in cervical cancer screening and diagnosis, with a particular focus on enhancing the precision of early diagnosis. The obstacles and benefits of employing AI in the diagnosis and treatment of cervical cancer are also covered.
  • Keywords: Cervical cancer, Human papillomavirus (HPV), Artificial intelligence, Precision diagnosis

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