<![CDATA[Cervical cancer remains a leading cause of mortality among women worldwide, primarily due to challenges in early and accurate detection. Conventional screening methods like Pap smears are subject to human error and have moderate sensitivity. This study aimed to develop and validate a novel, non-invasive diagnostic platform combining Raman spectroscopy with artificial intelligence (AI) for the rapid and highly accurate detection of early-stage cervical cancer biomarkers. The objective was to create a system that could overcome the limitations of current screening techniques. We collected cervical cell samples from clinically diagnosed healthy, pre-cancerous (CIN I-III), and cancerous patients. Raman spectroscopy was used to acquire high-resolution biochemical fingerprints from these samples. A custom-developed convolutional neural network (CNN) was then trained on the spectral data to learn and identify subtle biomarker-associated patterns indicative of neoplastic transformation. The AI-augmented system achieved a diagnostic accuracy of 96.5%, with a sensitivity of 98% and a specificity of 95% in differentiating high-grade lesions and cancerous samples from healthy ones. The model successfully identified key spectral shifts related to nucleic acid and protein conformational changes, correlating them with disease progression.]]>
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