ArticleJournal of mid-life health
A Prospective Study to Assess the Utility of Artificial Intelligence-driven Cervical Screening Using Smart Scope versus Conventional Method.
Article in Journal of mid-life health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from high-risk human papillomavirus infection. Approximately 90% of cervical cancers can be prevented due to the disease's slow progression, which allows for a 10-year window for the detection and treatment of precancerous lesions. The World Health Organization called for global action toward the elimination of cervical cancer. One of the main strategies for cervical cancer elimination is to achieve screening coverage of at least 70% of women aged 35-45 years and to ensure that 90% of women diagnosed with precancerous lesions or invasive cervical cancer receive appropriate treatment by 2030. One of the main strategies for cervical cancer elimination is to achieve screening coverage of at least 70% of women aged 35-45 years and to ensure that 90% of women diagnosed with precancerous lesions or invasive cervical cancer receive appropriate treatment by 2030. Aims and Objectives: The aims of the study were a comparative analysis of conventional Pap smear cytology and artificial intelligence (AI)-based analysis using Smart Scope for screening of cervical cancer. Materials and Methods: A prospective, cross-sectional study was conducted among 128 women at a tertiary care center. Participants underwent cervical screening using Smart Scope imaging, followed by AI-based analysis. The findings were compared with conventional methods using appropriate statistical tests. Results: In this prospective cross-sectional pilot study involving 128 samples, AI-assisted screening demonstrated a high specificity (96.0%) and negative predictive value (99.2%) for cervical cancer detection, effectively identifying 89.8% of cases as negative for intraepithelial lesion or malignancy. AI enabled rapid image analysis and gynecologist-dependent diagnostic variability and minimized the subjectivity inherent in human interpretation. AI enabled rapid image analysis, reduced gynecologist-dependent diagnostic variability, and minimized the subjectivity inherent in human interpretation. Conclusions: AI-based image analysis shows promise as an adjunct tool in cervical cancer screening but lacks the reliability to function as a standalone modality. Further refinement and integration with the clinical context are warranted to improve its screening effectiveness.
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