A comprehensive review for machine learning based human papillomavirus detection in forensic identification with...

Area of Science:

• Oncology
• Virology
• Medical Informatics

Background:

• Human papillomavirus (HPV) is a primary cause of cervical cancer and other malignancies.
• Increasing HPV vaccination rates highlight the need for advanced diagnostic tools.
• Current HPV testing requires high sensitivity and specificity, with added value in source tracing.

Purpose of the Study:

• To review recent advancements in HPV testing methodologies.
• To explore the integration of machine learning (ML) and deep learning (DL) in HPV diagnostics.
• To assess the potential of AI in improving HPV infection detection and source identification.

Main Methods:

• Review of recent literature on HPV testing techniques.
• Analysis of studies incorporating machine learning and deep learning algorithms for HPV data analysis.
• Examination of AI applications in medical examination data interpretation for HPV.

Main Results:

• Machine learning and deep learning show promise in enhancing the sensitivity and specificity of HPV detection.
• AI models can potentially analyze complex medical data to identify patterns associated with HPV infection.
• Integration of AI may facilitate more effective tracing of HPV infection origins.

Conclusions:

• Combining advanced HPV testing with machine learning and deep learning offers significant potential for improved cancer screening and prevention.
• AI-driven approaches can optimize the use of medical examination data for public health.
• Future research should focus on validating and implementing these AI-enhanced HPV testing strategies.